diff --git a/.claude/rules/docs.md b/.claude/rules/docs.md
new file mode 100644
index 0000000..7ff2e36
--- /dev/null
+++ b/.claude/rules/docs.md
@@ -0,0 +1,12 @@
+---
+paths:
+  - "docs/**"
+  - "mkdocs.yml"
+---
+
+# Documentation Rules
+
+- Write docs in Markdown with clear headings and code examples.
+- Update `mkdocs.yml` navigation when adding new docs.
+- Keep `README.md` concise, and move long explanations to `/docs`.
+- Use Python code fences (```python … ```) in examples.
diff --git a/.claude/rules/git-workflow.md b/.claude/rules/git-workflow.md
new file mode 100644
index 0000000..9edcf9b
--- /dev/null
+++ b/.claude/rules/git-workflow.md
@@ -0,0 +1,6 @@
+# Git Workflow
+
+- Do not call `git commit`.
+- Staging and inspection commands (`git status`, `git diff`, `git log`) are fine.
+- Prefix commits with `uv run` so pre-commit hooks use the correct environment, e.g.
+  `uv run git commit -m "message"`.
diff --git a/.claude/rules/pydantic-usage.md b/.claude/rules/pydantic-usage.md
new file mode 100644
index 0000000..7da8b01
--- /dev/null
+++ b/.claude/rules/pydantic-usage.md
@@ -0,0 +1,12 @@
+---
+paths:
+  - "**/*.py"
+---
+
+# Pydantic Rules
+
+- Use `pydantic.BaseModel` for request/response schemas.
+- For configuration, use `pydantic_settings.BaseSettings` (v2).
+- Always provide default values or use `...` for required fields.
+- Use model validators (`@field_validator`, `@model_validator`) instead of custom init logic.
+- Keep models small and focused on one purpose.
diff --git a/.claude/rules/python-run.md b/.claude/rules/python-run.md
new file mode 100644
index 0000000..b42b264
--- /dev/null
+++ b/.claude/rules/python-run.md
@@ -0,0 +1,13 @@
+---
+paths:
+  - "**/*.py"
+  - "run/**"
+---
+
+# Running Python code
+
+- **Do not** run `python3 -c` directly.
+- Always invoke Python code using `uv run python <script>` or `uv run <module>` when
+  executing project scripts.
+- Use the `uv` environment so that dependencies from `uv.lock` are respected.
+- This applies to both interactive code execution and testing.
diff --git a/.claude/rules/python-style.md b/.claude/rules/python-style.md
new file mode 100644
index 0000000..5ef97cf
--- /dev/null
+++ b/.claude/rules/python-style.md
@@ -0,0 +1,14 @@
+---
+paths:
+  - "**/*.py"
+---
+
+# Python Coding Rules
+
+- Use python 3.12 conventions: use `type | None` instead of `Optional` etc
+- Use type hints everywhere (PEP 484).
+- Use snake_case for functions and variables, PascalCase for classes.
+- Avoid unused imports.
+- Use `async`/`await` where appropriate.
+- Always keep functions small and composable.
+- Avoid using hasattr at all costs, it's not safe
diff --git a/.claude/rules/tests.md b/.claude/rules/tests.md
new file mode 100644
index 0000000..ae22c72
--- /dev/null
+++ b/.claude/rules/tests.md
@@ -0,0 +1,15 @@
+---
+paths:
+  - "test/**/*.py"
+---
+
+# Testing Guidelines
+
+- Use `pytest` with descriptive test function names.
+- Prefer fixtures over global state.
+- For async code, use `pytest-asyncio`.
+- Ensure test coverage for:
+  - Pydantic model validation
+  - Env config via `BaseSettings`
+  - Error cases and edge cases
+- Run tests with `uv run pytest test`.
diff --git a/.claude/rules/uv-deps.md b/.claude/rules/uv-deps.md
new file mode 100644
index 0000000..b8de0e3
--- /dev/null
+++ b/.claude/rules/uv-deps.md
@@ -0,0 +1,10 @@
+---
+paths:
+  - "pyproject.toml"
+  - "uv.lock"
+---
+
+# Dependency Management
+
+- Pin versions in `pyproject.toml` for reproducibility.
+- Run `uv pip check` to validate the dependency tree.
diff --git a/.cursorignore b/.cursorignore
new file mode 100644
index 0000000..cd6e3fd
--- /dev/null
+++ b/.cursorignore
@@ -0,0 +1,25 @@
+.venv/
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+__pycache__/
+*.egg-info/
+site/
+dist/
+build/
+
+# Trained artifacts and large outputs
+models/
+reports/
+nbs/
+debug/
+*.pth
+*.bin
+*.pt
+*.pkl
+*.joblib
+*.parquet
+*.gz
+
+# HuggingFace / spaCy caches (outside repo but common nearby)
+.cache/
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..b79ef27
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,19 @@
+version: 2
+
+updates:
+-   package-ecosystem: uv
+    directory: /
+    schedule:
+        interval: weekly
+    open-pull-requests-limit: 5
+    groups:
+        python-minor-patch:
+            update-types:
+            -   minor
+            -   patch
+
+-   package-ecosystem: github-actions
+    directory: /
+    schedule:
+        interval: weekly
+    open-pull-requests-limit: 5
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
new file mode 100644
index 0000000..6a3d766
--- /dev/null
+++ b/.github/workflows/test.yml
@@ -0,0 +1,34 @@
+name: test
+
+on:
+    pull_request:
+    push:
+        branches: [main]
+
+jobs:
+    pytest:
+        runs-on: ubuntu-latest
+        timeout-minutes: 60
+        steps:
+        -   uses: actions/checkout@v5
+        -   name: Install uv
+            uses: astral-sh/setup-uv@v7
+            with:
+                version: "0.10.12"
+                enable-cache: true
+
+        # The dev extra pins en_core_web_lg, so the tests covering tokenization, span
+        # mapping and the HF embedding path actually run here. They skip themselves when
+        # the model is absent, which is how a removed transformers API once reached main.
+        -   name: Install dependencies (including dev)
+            run: uv sync --extra dev
+
+        -   name: Cache Hugging Face weights
+            uses: actions/cache@v4
+            with:
+                path: ~/.cache/huggingface
+                key: hf-${{ runner.os }}-${{ hashFiles('uv.lock') }}
+                restore-keys: hf-${{ runner.os }}-
+
+        -   name: Run tests
+            run: uv run pytest test
diff --git a/.gitignore b/.gitignore
index ce7faeb..6a92d86 100644
--- a/.gitignore
+++ b/.gitignore
@@ -165,6 +165,16 @@ cython_debug/
 nbs/
 debug/
 
-reports/
-paper/
+# Anchored: measurement output at the repo root. Unanchored, this also matched the
+# package source directory pelinker/reports/, which then never reached the repository.
+/reports/
+models/
+**/models/
+*.pth
+*.bin
+*.pt
+*.pkl
+*.joblib
 pelinker/store/*gz
+
+.env*
\ No newline at end of file
diff --git a/.pylintrc b/.pylintrc
deleted file mode 100644
index 767731a..0000000
--- a/.pylintrc
+++ /dev/null
@@ -1,7 +0,0 @@
-[TYPECHECK]
-signature-mutators=click.decorators.option,
-                   click.decorators.argument,
-                   click.decorators.version_option,
-                   click.decorators.help_option,
-                   click.decorators.pass_context,
-                   click.decorators.confirmation_option
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index e69de29..08a6f13 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -0,0 +1,325 @@
+# Changelog
+
+All notable changes to this project are documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+
+### Added
+
+- A scoring-only reference inventory (`pelinker/eval/reference.py`). `fold_map` folds
+  each KB id onto its class representative: converse members onto their canonical member
+  (tier 0), then along the KB-implied identities of an equivalences file (tier 1,
+  `load_equivalences`). The file is validated against the KB: unknown tiers, self-maps,
+  ids with two targets, unknown ids and cycles are refused. Training never reads it.
+- `data/curated/equivalences.csv`: the KB-implied identities, i.e. converses minted for
+  verbs the KB later marks symmetric.
+- `run/eval/gold_review_sheet.py` reads agreement in the folded space:
+  - `export` adds `other_label` and `agrees_direction`; `agrees` compares folded ids;
+  - `agrees_direction` is filled only where the relation agrees, and a symmetric relation
+    agrees in any direction;
+  - `--equivalences` on `export` and `agreement` (the latter also takes `--kb-csv-path`);
+  - `kappa_report(…, fold=)` adds `kappa_entity_id_folded` beside the raw κ;
+  - `export` refuses to overwrite an existing sheet, which may hold verdicts.
+- **Class views** (`pelinker/kb/classes.py`) decide what counts as one class of the KB,
+  without editing the KB. Each is a projection of the pairs KB's own declarations:
+  - `raw`: the matched label;
+  - `rel`: the canonical relation;
+  - `reldir`: the canonical relation plus the mention's direction relative to it.
+
+  In `reldir`, a passive mention is `(R, inverse)` whether or not the KB holds a converse
+  entry for `R`. Under raw labels it was its own class in one case and shared the active
+  class in the other.
+
+  `add_view_columns` adds `relation`, `relation_direction` and `entity_class` to a mention
+  frame. It refuses labels the KB does not hold. Judgements about the KB (which entries
+  mean the same thing) are deliberately not accepted, so they cannot reach training
+  labels. `canonical_id_map`, `symmetric_ids` and `require_canonical_kb` moved here;
+  `pelinker.eval.kb` re-exports them.
+- `class_view` / `class_kb_path` on `ClusteringOptimizationConfig` and `LinkerFitConfig`;
+  `--class-view` / `--class-kb-path` on `pelinker-model-selection`,
+  `pelinker-dim-selection` and `pelinker-scale-curve`; and `class_view` / `class_kb_path`
+  on `pelinker-fit`, where the KB path defaults to `kb_path`.
+  - `load_selection_frame` applies the view.
+  - The grid ARI (`evaluate_cluster_size_grid`) and the fit ARI
+    (`compute_clustering_fit_metrics`) score against `entity_class` when it is present.
+  - A view enters the search checkpoint fingerprint, with the KB by content hash. A `raw`
+    run keeps the fingerprint it had before.
+- A direction per cluster.
+  - Under a class view, the fit's catalog composition is read between canonical
+    relations: the two voices of one relation are one entity whose clusters differ in
+    direction, not two entities a cluster merged.
+  - Each catalog cluster carries `direction_mix` and `dominant_direction`
+    (`kb_out.annotate_cluster_directions`, `cluster_direction_summary`).
+  - `kb_out_to_reldir_map` returns (input-KB id, direction) per KB-out id.
+  - `Linker.cluster_direction` holds the map, and `Linker.predict` and `/link` emit
+    `direction_predicted` for a linker fitted with a view.
+  - `/info` reports `emits_direction`.
+  - The catalog's fit provenance records `class_view` and `class_kb_sha256`.
+  - Mention `direction` and the view columns now travel into
+    `Linker.training_cluster_frame`.
+- `docs/user_guide/api.md`: the HTTP routes, request fields and entity-row fields,
+  including how a KB-out id reaches the input KB.
+- `run/analysis/weak_label_check.py` reports classes per view and the mention mass by
+  direction relative to the canonical relation. A label the KB does not hold is now a
+  violation.
+- Gold evaluation pipeline (`run/eval/`, `pelinker/eval/`): document sampling with
+  `primary` / `double` / `reserve` roles, LLM pre-annotation, a human review round trip
+  with Cohen's κ, and reference baselines (lexical lemma, sentence-encoder k-NN, an LLM
+  linker, and a fitted artifact) scored through one harness in two regimes — end-to-end
+  span+link, and linking-only over gold spans. Documented in
+  `docs/user_guide/evaluation.md`.
+- `eval` extra carrying the LLM provider SDKs. The linker itself never calls an LLM; only
+  the evaluation path does.
+- `openai` joins `gemini` and `anthropic` in the `pelinker.eval.llm` provider seam. The
+  agreement slice wants a second model *family*, and the LLM baseline a third model that
+  did not annotate the gold, so one SDK was never enough. Credentials stay per provider
+  (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GEMINI_API_KEY` / `GOOGLE_API_KEY`) and the
+  response cache is keyed on the provider, so one provider's answer is never replayed for
+  another.
+- `run/preprocessing/derive_inverse_pairs.py` links converse pairs across the whole KB —
+  declared `owl:inverseOf` plus three surface-form rules, each recorded in
+  `inverse_source` — and elects a canonical member per pair (`is_canonical`,
+  `canonical_entity_id`), so a passive mention has exactly one valid encoding and
+  orientation lives in `direction`.
+- A curator review loop for those derived pairs: unreviewed proposals are exported to
+  `<output>.pending_review.csv`, and `--review-csv` applies the verdicts — `accept` clears
+  the flag, `reject` unlinks the pair so each member is its own canonical form again. An
+  unreviewed wrong pair merges two distinct relations onto one id, and prompts, gold and
+  scores would then all agree with the mistake.
+- Symmetric relations: `extract_properties_ro.py` emits `is_symmetric` (declared
+  `owl:SymmetricProperty`, or defined by the chain `inverse(P) ∘ P`), and the pairs KB
+  carries it. The surface-form rules never pair a symmetric entry — "connected to" looks
+  passive but has no converse. The annotation prompt marks symmetric labels, and
+  `annotate_llm.py` sets an oriented answer on one to `symmetric`, flagged
+  `direction_coerced`. Entries RO does not cover are marked symmetric through a curated
+  list (`derive_inverse_pairs.py --symmetric-csv`, default `data/curated/symmetric.csv`).
+- The passive surface rule recognises irregular participles ("bound by" ↔ "binds to"),
+  not only `-ed` / `-en` forms.
+- The curated KB carries converse entries (`X-ed by`) for the GeneWays verbs, so a
+  passive mention matches its own entry and folds onto the active relation with
+  `direction=inverse`; `stimulates` and `interrupts` are restored. The converse pairs go
+  through the curator review loop like every other derived pair.
+- `score_predictions_against_ground_truth` and both harness regimes take `canonicalize`,
+  applied to predicted **and** gold ids after the KB-out → KB-in bridge.
+- `reasoning_effort` in `pelinker.eval.llm.complete` (openai only) and
+  `annotate_llm.py --reasoning-effort`. It is part of the cache key only when set, so
+  existing caches replay unchanged, and it is added to the default annotator tag
+  (`<model>@<effort>`).
+- `annotate_llm.py` accepts an object wrapping the requested array
+  (`{"annotations": [...]}`), since object-only JSON modes produce that shape. It also
+  reports `duplicate_occurrence`, a mention re-emitted after every occurrence is taken,
+  separately from `surface_not_found`, quoted text absent from the abstract.
+
+### Fixed
+
+- Truncated LLM answers were cached as final. A response cut off at the output limit
+  would replay on every later run as an unparsable document with no candidates, and
+  raising the limit would not retry it. Reasoning models hit this because their hidden
+  reasoning counts against the output limit. `pelinker.eval.llm` now raises
+  `LLMIncompleteError` and caches nothing when the provider reports truncation (openai
+  `status`, gemini `MAX_TOKENS`, anthropic `max_tokens`) or returns empty text.
+  `annotate_llm.py` reports those documents as `truncated` and leaves them out of the
+  output, so they are retried on the next run.
+
+- Corpus embedding (`embed_kb_corpus`, `pelinker-fit` stage A) crashed at the second read
+  buffer with "generator already executing": the stream that re-attaches the peeked
+  first chunk closed over a variable later rebound to that same stream. Any input longer
+  than one `input_buffer_rows` pass was affected. The stream is now built by a
+  module-level `prepend_first`.
+
+- Agreement (κ) is computed over the documents both annotators covered. It previously
+  walked every document of the first annotator, so with a second annotator on the `double`
+  slice alone — the normal case — each hit on an unannotated document counted as a
+  disagreement and κ collapsed for a reason unrelated to agreement. The report now carries
+  `n_docs_shared`, `n_only_a` and `n_only_b`.
+- κ is symmetric: a span only the second annotator marked now enters as an explicit `∅` on
+  the first annotator's side, so missing a mention and inventing one are penalized alike.
+  The docstring had claimed this; only one direction was implemented.
+- The review sheet surfaces spans only the second annotator proposed (`origin=other`), and
+  `import` adopts the accepted ones from `--other`. Verified gold could previously never
+  exceed the first annotator's recall.
+- Review rows for documents the second annotator never saw read `not-covered` instead of
+  an empty cell that looks like a disagreement.
+- Baseline ids are scored in canonical space. Gold carries the canonical member of each
+  converse pair while the baselines choose from the whole KB, so a correct "regulated by"
+  link was scored as an error — measuring the vocabulary's redundancy rather than the
+  system.
+- The linking-only regime applies the KB-out → KB-in bridge, so a fitted artifact's minted
+  ids are comparable there as they already were end-to-end.
+- `.gitignore` no longer excludes the package source directory `pelinker/reports/`. The
+  entry was the unanchored `reports/`, which matches a directory of that name at **any**
+  depth, so four modules that ten others import were never in the repository and a fresh
+  clone could not import `pelinker` at all. The pattern is now anchored to the repo root,
+  where measurement output lives.
+- `pelinker/reports/` and `pelinker/text/` gained `__init__.py`. As implicit namespace
+  packages they imported fine but could not be collected by mkdocstrings, so
+  `mkdocs build --strict` failed and their modules had no API reference pages.
+- `run/loop.embed.kb.corpus.sh` invokes `uv run python` rather than bare `python`, so the
+  batch grid uses the locked environment like every other entry point.
+- Refusing a KB that predates the pair derivation now names the file that was passed, says
+  that a `*.inverse.csv` is the derivation's *input*, and points at the sibling
+  `*.pairs.csv` when one exists. Several usage blocks had shown exactly the KB the check
+  rejects.
+
+### Security
+
+- Raised `transformers` to `>=5.5.0,<6`, closing GHSA-29pf-2h5f-8g72 (RCE, patched in
+  5.3.0), GHSA-fgcw-684q-jj6r (arbitrary code execution in the LightGlue model loading
+  path, patched in 5.5.0) and GHSA-69w3-r845-3855 (arbitrary code execution in `Trainer`).
+- Raised the `torch` floor to `>=2.13.0` (GHSA-rrmf-rvhw-rf47, memory corruption in
+  `torch.jit.script`), and relocked `pillow` 12.3.0 (13 advisories) and `setuptools`
+  83.0.0 (GHSA-h35f-9h28-mq5c).
+- Added `.github/dependabot.yml` (weekly `uv` and `github-actions` updates); there was no
+  Dependabot configuration before.
+
+### Changed
+
+- Corpus embedding spends less CPU time between encoder passes: spaCy runs batched via
+  `nlp.pipe` without NER, lemma matching uses a per-holder index, and word→subword span
+  mapping uses binary search. Output is unchanged.
+
+- The `gpu` extra is split into `gpu-cu13` (PyPI torch + `cupy-cuda13x[ctk]`) and
+  `gpu-cu12` (torch from the PyTorch `cu126` index + `cupy-cuda12x[ctk]`); they are
+  mutually exclusive. Pick by the CUDA version `nvidia-smi` reports.
+
+- **`pelinker-fit` and the selection CLIs default to `class_view=reldir`** and therefore
+  need the pairs KB (`properties.synthesis.2.pairs.csv`): as `kb_path` for the fit, or as
+  `--class-kb-path` for the searches. Pass `class_view=raw` / `--class-view raw` to
+  reproduce earlier fits and searches exactly. Hyperparameters selected against raw labels
+  do not carry over to a `reldir` fit, because the objective's reference partition
+  differs.
+
+- The gold sampler (`run/eval/sample_gold.py`) draws only eligible abstracts:
+  publication year, length and sentence bounds, English, not a duplicate, and not in the
+  fit corpus (`--exclude-corpus`). Each dropped row is counted by reason. It stratifies on
+  mentions detected with the fit's own definition (`pelinker.text.mentions`) rather than
+  raw lemma-window counts, which mostly tracked abstract length. Tail strata (rare labels)
+  are over-allocated. Each document records its inclusion probability `p_incl`, and the
+  sample grows in batches with `--extend` (stable ids, roles per batch).
+  `run/eval/audit_sample.py` re-checks a sample and reports KB coverage per batch.
+- Gold hits carry `surface_anchored`: `false` marks a paraphrase ("leads to" for
+  `causes`), which is kept and scored as its own slice. `gold_review_sheet.py import`
+  records each hit's `verdict` and writes `<output>.verification.json`: accept, fix and
+  reject counts and the correction rate, split by anchored and paraphrase.
+  `audit_sample.py` reports the paraphrase share and the verification summary.
+- `run/analysis/weak_label_check.py`: acceptance check for a stage-(A) parquet. It fails
+  on multi-labelled or nested spans, on relation/converse collisions, and on a symmetric
+  relation with an inverse direction.
+- `run/loop.embed.kb.corpus.sh` refuses to overwrite an existing parquet. It adds
+  `--models`, `--layers`, `--max-input-buffers` and `--no-gpu`, fails fast, and warns when
+  the KB has no `is_symmetric` column.
+- `annotate_llm.py` rejects hits on verb-predicate labels whose span has no verb
+  (`non_verbal`), after the response cache (`--verbal-only`, on by default).
+- Weak supervision labels verb predicates from the dependency parse instead of lemma
+  windows (`pelinker/text/predicates.py`). Each verb mention gets at most one KB label,
+  chosen by voice: a passive mention takes the KB's converse entry ("activated by"), even
+  with words between the verb and its agent or no agent at all, and falls back to the
+  active entry with `direction=inverse` when the KB has no converse. Symmetric relations
+  are never flipped; nouns ("controls") and prenominal modifiers without an object
+  ("activated T cells", "binding sites") are not labelled. A label's preposition must
+  attach to the verb — "developed complications" is not `develops from` — except the
+  agent of a passive `by` label. The embedded span is the verb, extended to an adjacent label
+  adverb ("negatively regulated") — the same kind of contiguous window prediction
+  proposes, so prediction stays embedding-only. Non-verb labels ("part of") keep lexical
+  matching, and every mention site keeps one label. Mention parquets gain `direction` and
+  `surface_rule`. Hyperparameters selected on mentions extracted before this change were
+  fitted to contradictory labels and should be re-selected.
+
+- **BREAKING:** `min_cluster_size` selection is rebuilt. The DBCV+ARI objective was an
+  arithmetic mean of two per-curve **min–max normalized** metrics, and min–max normalizes
+  *range*, not noise: a metric that barely moved got its noise rescaled to match the other
+  and then handed 50% of the objective. The combined std was also computed from the *raw*
+  metrics while the mean used the normalized ones, so every consumer of that std — the
+  `lower_bound` discount, the smoothing weights, the leaderboard tie-break, the persisted
+  `score_std_at_chosen` — was mixing units.
+  - `grid_objective` is now `dbcv_ari_geomean`: `sqrt(max(dbcv, 0) * max(ari, 0))`. Its
+    ranking is invariant to rescaling either metric, so no normalization step is needed at
+    all, and it is conjunctive — a high DBCV can no longer buy off a near-zero ARI.
+    `dbcv_ari_mean_minmax` and `dbcv_ari_mean_raw` are removed.
+  - Metrics are combined **per bootstrap sample and then pooled**, not pooled and then
+    combined, so DBCV/ARI correlation is handled correctly and the samples stay paired.
+  - The plateau/derivative selector is replaced by a one-standard-error rule using *paired*
+    standard errors: take the argmax, then move to the largest `min_cluster_size` reachable
+    through consecutive grid points within `grid_one_se_k` (default 1.0) standard errors of
+    it. Uncertainty can now only break a statistical tie; it can never promote a point with
+    a worse mean, which `mean - 1*std` did. `optimization_method`, `grid_plateau_fraction`
+    and `grid_derivative_rel_tol` are removed; `grid_one_se_k`,
+    `grid_one_se_contiguous` and `grid_one_se_min_samples` replace them.
+  - The rule is skipped below `grid_one_se_min_samples` (default 6) bootstrap samples. A
+    standard error estimated from three numbers is mostly noise; measured by
+    leave-one-sample-out on this repo's grid exports, acting on it made the choice *less*
+    reproducible, and the slide only starts paying for itself around 6–8 samples.
+  - The rule narrows the leave-one-sample-out spread of the chosen `min_cluster_size`;
+    measure it on your own grid exports with `pelinker-replot --grid-one-se-k`.
+- **BREAKING:** the outer candidate ranking uses the same clipped geometric mean as the
+  grid. It previously min–max normalized **across the leaderboard**, so adding an
+  irrelevant candidate could reorder the good ones; scores are now computed per row and are
+  comparable across runs. `attach_outer_scores` / `pick_best_row` lose `use_minmax`, and
+  `CHECKPOINT_VERSION` is bumped to 2 because `singleton_scores_by_key` persists these
+  values and a resumed v1 checkpoint would mix the two scales.
+- Search summaries read **ARI at the pooled `min_cluster_size`**. DBCV was already read
+  there, but ARI came from each sample's own best size, so `outer_score` combined two
+  metrics measured at different hyperparameters.
+- The combined-objective panel now renders in real runs. Runners passed
+  `chosen_min_cluster_size` and no `grid_solve`, which suppressed the solve entirely, so
+  the fourth axis shipped empty and titled "Grid objective (unavailable)" in every run
+  except `pelinker-replot`. The panel also gained the ±1 SE band, the decision boundary,
+  the tied grid points and the argmax marker; single-sample `plot_metrics` gained the panel
+  and the chosen-value line; and the DBCV-vs-ARI scatter now draws the Pareto front.
+- `pelinker-replot` gained `--grid-one-se-k` and no longer resets unrelated solver knobs to
+  their defaults when only `--grid-cluster-count-reward` / `--grid-n-entities` are given.
+- **BREAKING:** `pelinker`'s flat module layout is reorganised into domain subpackages —
+  `core/`, `text/`, `data/`, `embed/`, `clustering/`, `screener/`, `linker/`, `kb/`,
+  `search/`, `reports/`. Every import path under `pelinker.*` changed; the console-script
+  entry points did not.
+- `pelinker.analysis` is gone. It fused three unrelated concerns, now split into
+  `text/lexical.py`, `screener/evaluation.py`, `clustering/metrics.py`,
+  `data/frames.py` and `search/grid_solver.py`.
+- `pelinker.io` is now `pelinker.data` — the old name shadowed the stdlib `io` module
+  inside the package namespace.
+- `pelinker.reporting` is split into `reports/schema.py` (the dataclasses),
+  `reports/paths.py` (basenames and path builders), `reports/io.py` (JSON read/write)
+  and `reports/summary.py` (flat-row aggregation).
+- `pelinker.util` is split into `text/models.py`, `text/tokenize.py`,
+  `text/chunking.py` and `text/embed.py`, plus `core/paths.py` and `kb/registry.py`.
+- `ChunkMapper` and `ReportBatch` move from `core/onto.py` into `text/`. They called into
+  the text pipeline from inside their methods purely to dodge the `util` ⇄ `onto` import
+  cycle; that cycle is now gone and `core/onto.py` imports nothing from `pelinker`.
+- **BREAKING:** the default spaCy pipeline is now `en_core_web_lg` instead of
+  `en_core_web_trf`. `en_core_web_trf` requires `spacy-transformers`, which pins
+  `transformers<4.53.3` and therefore cannot coexist with the patched `transformers`
+  releases above. pelinker uses spaCy only for tokenization, `lemma_`, `tag_` and `pos_`,
+  so a non-transformer pipeline covers the required surface — but POS/lemma accuracy
+  differs, so re-score against `data/ground_truth` before relying on existing thresholds.
+- Raised `sentence-transformers` to `>=5.6,<6`, the first release permitting
+  `transformers` 5.x.
+
+### Fixed
+
+- `matplotlib`, `seaborn`, `plotly` and `pySankey` are imported at module level by
+  library code but were declared only in the `dev` extra, so four of the seven console
+  scripts (`pelinker-model-selection`, `-dim-selection`, `-replot`, `-scale-curve`)
+  raised `ModuleNotFoundError` on a plain install. They are runtime dependencies now.
+- The `dev` extra pins the `en_core_web_lg` wheel instead of relying on
+  `spacy download`, which any subsequent `uv sync` prunes — silently re-skipping every
+  test that needs the model.
+- `ruff` was not reporting unused imports (no `[tool.ruff.lint]` section, so the
+  pre-commit defaults applied); ~100 had accumulated. Now configured and cleaned.
+- `pelinker-serves` segfaulted on import. TensorFlow (pulled in via `tf-keras` for
+  ParametricUMAP) and torch's bundled `triton` conflict at the native level: whichever
+  order left `triton.runtime` importing after TensorFlow crashed the interpreter.
+  `pelinker/__init__.py` now imports torch and `triton.runtime` first.
+- `text_to_tokens_embeddings` used `tokenizer.batch_encode_plus`, which was removed in
+  transformers 5.x; it now uses the standard `tokenizer(...)` call. This path was not
+  covered by CI because the tests exercising it skip when the spaCy model is absent.
+
+### Removed
+
+- `install-cuda121.sh`: GPU setup is `uv sync --extra gpu-cu13` or `--extra gpu-cu12`.
+- `mypy` from the `dev` extra and the unused `.pylintrc` — neither was wired into
+  pre-commit or CI.
+- `pip` and a duplicate `cupy-cuda12x` from the runtime dependencies. `cupy` is not
+  imported anywhere in the package; it remains available through the `gpu` extra.
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..c3b27c9
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,91 @@
+# PELinker — Claude Code Instructions
+
+Property/entity linker for BERT-like models. The pipeline embeds corpus mentions, clusters
+them (PCA → UMAP → HDBSCAN), and serializes a `Linker` artifact. Batch linking and a
+FastAPI server expose inference.
+
+## Package layout
+
+| Area | Path | Role |
+|------|------|------|
+| Config / paths | `pelinker/core/` | Hydra-facing config, KB metadata, scaling |
+| Text / embeddings | `pelinker/text/`, `pelinker/embed/` | Tokenization, transformer loading, corpus embedding |
+| Data | `pelinker/data/` | Parquet/JSON frames, fusion |
+| Clustering | `pelinker/clustering/` | Fit pipeline, grid metrics, transforms |
+| Hyperparam search | `pelinker/search/` | Model/dim selection, scale curve, grid solver |
+| Linker training | `pelinker/linker/` | Cluster training, distillation, KB lemma |
+| KB | `pelinker/kb/` | Ground-truth spans + scoring, KB-out catalog and id bridge, class views (`classes.py`) |
+| Screener | `pelinker/screener/` | Negative / manifold-OOV screening |
+| Evaluation | `pelinker/eval/` | Gold harness, reference baselines, canonical KB view, LLM provider seam |
+| Reports | `pelinker/reports/` | Report schemas, paths, summaries |
+| Store | `pelinker/store/` | Packaged model resources |
+| CLIs | `pelinker/cli/` | `pelinker-fit`, `pelinker-serves`, etc. |
+| Scripts | `run/` | Preprocessing, analysis, `run/eval/` gold pipeline — see @run/README.md |
+
+## Environment (uv only)
+
+This project uses **uv** for all Python environment management. Do not use Poetry, bare
+`python`/`python3`, `pip install`, or bare `pytest`.
+
+```bash
+# First-time / refresh env
+uv sync --extra dev          # CI + local dev (pytest, pre-commit, en_core_web_lg)
+uv sync --extra docs         # mkdocs build
+uv sync --extra eval         # gold pipeline: LLM provider SDKs (run/eval/*)
+uv sync --extra gpu-cu13     # CUDA torch + CuPy; driver CUDA 13.x (nvidia-smi)
+uv sync --extra gpu-cu12     # same for a CUDA 12.x driver; exclusive with gpu-cu13
+# extras are exclusive: name them together (--extra dev --extra eval) or the omitted
+# ones are uninstalled
+
+# Run code
+uv run python run/script.py
+uv run pelinker-fit ...
+uv run pelinker-serves
+uv run pytest test
+uv run pre-commit run --all-files
+uv run pre-commit install
+
+# After dependency edits
+uv lock && uv sync --extra dev && uv pip check
+```
+
+Prefix git commits with `uv run` so pre-commit hooks use the correct environment, e.g.
+`uv run git commit -m "message"`.
+
+## Linting and formatting
+
+Pre-commit runs Ruff check + format (see `.pre-commit-config.yaml`). Ruff config in
+`pyproject.toml` selects `E4`, `E7`, `E9`, `F` and ignores `E722`.
+
+## Testing
+
+- Run: `uv run pytest test`
+- Tests marked `heavy` skip when spaCy `en_core_web_lg` or HF weights are absent.
+- The `dev` extra pins `en_core_web_lg` so CI runs tokenization tests without a separate
+  `spacy download`.
+
+## Gold evaluation
+
+`run/eval/` is the human-verified gold pipeline; `docs/user_guide/evaluation.md` is the
+guide. Two invariants:
+
+- Every step consumes the **pairs** KB (`data/derived/properties.synthesis.2.pairs.csv`,
+  built by `run/preprocessing/derive_inverse_pairs.py`). A KB without `is_canonical` is
+  refused, not tolerated — a fallback would change what the gold means.
+- Ids are compared in canonical space on both sides (`pelinker.eval.kb.canonical_id_map`),
+  so a converse-member answer scores as the relation it names.
+
+Measured numbers belong in the measurement writeup, not in this repo: point
+`--report-dir` outside the working tree.
+
+## Gotchas
+
+- `pelinker/__init__.py` imports `torch`/`triton` before TensorFlow to avoid a native
+  segfault when ParametricUMAP loads both runtimes.
+- Use `uv sync --extra dev`, not `--all-groups` (no `[dependency-groups]` in
+  `pyproject.toml`).
+
+## Conventions
+
+Scoped rules live in `.claude/rules/` (Python style, tests, deps, docs, git workflow).
+For pipeline/CLI details see @run/README.md.
diff --git a/README.md b/README.md
index 8cc0be9..c8d0065 100644
--- a/README.md
+++ b/README.md
@@ -1,73 +1,118 @@
-# PELinker (Property Entity LINKER) <img src="https://raw.githubusercontent.com/growgraph/pelinker/refs/heads/main/static/favicon.ico" alt="PELinkder Logo" style="height: 32px; width:32px;"/>
+# PELinker <img src="https://raw.githubusercontent.com/growgraph/pelinker/refs/heads/main/static/favicon.ico" alt="PELinker logo" style="height: 32px; width:32px;"/>
 
-### Entity linking for BERT-like models
+**Property Entity Linker** — links relation mentions in scientific text to the properties
+of a knowledge base, using BERT-like encoders.
 
-![Python](https://img.shields.io/badge/python-3.10-blue.svg) 
-[![License](https://img.shields.io/badge/license-BSD--3--Clause-blue?style=flat-square)](https://img.shields.io/badge/license-BSD--3--Clause-blue?style=flat-square)
+![Python](https://img.shields.io/badge/python-3.10.6%2B-blue.svg)
+[![License](https://img.shields.io/badge/license-BSD--3--Clause-blue?style=flat-square)](LICENSE)
 [![pre-commit](https://github.com/growgraph/pelinker/actions/workflows/pre-commit.yml/badge.svg)](https://github.com/growgraph/pelinker/actions/workflows/pre-commit.yml)
+[![Docs](https://img.shields.io/badge/docs-growgraph.github.io-blue)](https://growgraph.github.io/pelinker/)
 
----
+## How it works
 
-## Overview
+1. **Embed** — find KB property mentions in a corpus (lemma windows, plus voice-aware
+   matching for verb predicates) and pool encoder activations into one vector per mention.
+2. **Fit** — reduce the vectors (PCA → UMAP), cluster them (HDBSCAN), and serialize the
+   result as a `Linker` artifact.
+3. **Link** — embed new text the same way and assign each mention to a cluster, with a
+   score and a predicted direction.
 
+The KB is a CSV of properties (`entity_id`, `label`, …) with converse pairs linked, so a
+passive mention resolves to the relation it names. [`data/README.md`](data/README.md)
+describes which KB files are curated and which are derived.
 
-Entity linking for BERT-like models
+## Install
 
+Requires [uv](https://docs.astral.sh/uv/) (`curl -LsSf https://astral.sh/uv/install.sh | sh`).
 
-## Developer notes
+```bash
+uv sync --extra dev
+```
+
+`dev` includes pytest, pre-commit and the spaCy `en_core_web_lg` model. Other extras:
+
+| Extra | Adds |
+|-------|------|
+| `docs` | MkDocs site |
+| `eval` | LLM provider SDKs for the gold pipeline (`run/eval/`) |
+| `preprocess` | ontology parsing for KB regeneration |
+| `gpu-cu13` / `gpu-cu12` | CUDA torch + CuPy; mutually exclusive |
 
-1. Make sure there is an available version of python specified in `pyproject.toml`, for example installed using pyenv.
-2. Install `uv` : `curl -LsSf https://astral.sh/uv/install.sh | sh`
-3. Run `uv sync --all-groups` to create a local environment with project dependencies specified in `uv.lock`
-4. Add a spacy language model `uv run spacy download en_core_web_trf`
-5. Set up `pre-commit` hooks:  `uv run pre-commit install`.
-6. To run `pre-commit` independently from `git commit`, run `uv run pre-commit run --all-files`
-7. To run tests run `pytest test`
+`uv sync` uninstalls every extra it is not given, so name them all in one command, e.g.
+`uv sync --extra dev --extra gpu-cu13`.
 
+**GPU.** Pick the extra by the "CUDA Version" that `nvidia-smi` reports — the driver's
+limit; an installed CUDA toolkit is not used: 13.x → `gpu-cu13`, 12.x → `gpu-cu12`. Check:
 
-NB.
-1. To run python scripts prefix the command with `uv run`, e.g. `uv run python script.py`
-2. To git commit also `uv run` prefix, e.g. `uv run git commit -m "first commit"` to make sure `pre-commit` hooks are used from the correct python environement. 
+```bash
+uv run python -c "import torch, cupy, cupy.cublas; print(torch.cuda.is_available(), cupy.cuda.runtime.getDeviceCount())"
+```
 
+## Usage
 
-## Testing against ground truth
+Prefix every command with `uv run`.
 
-Ground truth lives in `data/ground_truth` as `{"text": ..., "ground_truth": [{"itext",
-"a", "b", "entity_id"}, ...]}`. `pelinker-link-files` scores against it automatically
-whenever the input carries a `ground_truth` block:
+### Fit a linker
 
-```commandline
-uv run pelinker-link-files -m models/pelinker.pubmedbert.run1 \
-  -o reports/gt_score.json data/ground_truth/sample.0.gt.json
+```bash
+uv run pelinker-fit \
+  pipeline=both \
+  kb_path=data/derived/properties.synthesis.2.pairs.csv \
+  input_text_table_path=<corpus>.tsv.gz \
+  embeddings_parquet=<workdir>/corpus_pubmedbert_1.parquet \
+  model_path=<models>/pelinker.pubmedbert.run1 \
+  report_path=<workdir>/reports/run1
 ```
 
-The output JSON gains a `ground_truth_score` block:
+The corpus is a TSV/CSV of `pmid` and `text` (optionally gzipped). `pipeline` defaults to
+`embed_only`; `both` embeds and fits. Stages, parameters, and how the hyperparameter
+searches feed the fit: [`run/README.md`](run/README.md).
 
-- **Detection** — `precision` / `recall` / `f1` over character spans, matched by overlap
-  within a document. Unambiguous and comparable across model versions.
-- **Entity accuracy** — over matched spans only, and only where the ids are comparable.
-  Since the KB-out work the linker predicts *minted cluster ids* (`kb::C0007`) while the
-  gold file carries *input KB* ids (`PEL.000032`), so `n_id_comparable` may be 0 and
-  `entity_accuracy` `null`. That means undefined, not zero — read the detection numbers.
+### Link files
 
-Add `--kb-validation` for a `kb_lemma_validation` block: the rate at which a mention's
-predicted entity agrees with the entity its own lemma resolves to in the KB. That is a
-distant-supervision consistency check, not end-task accuracy.
+```bash
+uv run pelinker-link-files -m <models>/pelinker.pubmedbert.run1 -o out.json input.txt
+```
 
-Programmatic entry points: `pelinker.ground_truth.score_predictions_against_ground_truth`
-and `pelinker.linker_kb_lemma.aggregate_kb_lemma_validation`.
+Inputs are plain text (one document per file) or JSON with a `text` field.
 
-## Serialize Model
+### Serve
 
-"Train" a model on a corpus
+```bash
+uv run pelinker-serves model_file_spec=<models>/pelinker.pubmedbert.run1
+```
 
+FastAPI on port 8599; interactive docs at `/docs`. Without `model_file_spec` the server
+loads the packaged model. Routes and request fields: [HTTP API](docs/user_guide/api.md).
 
-- `uv run python run/save_model.py`
+## Evaluation
 
-### Run server
+**Against a file's own ground truth.** When an input carries a `ground_truth` block
+(`{"text": ..., "ground_truth": [{"itext", "a", "b", "entity_id"}, ...]}`, see
+`data/ground_truth/`), `pelinker-link-files` adds a `ground_truth_score` block:
 
-- `poetry run python run/serve`
+- **Detection** — precision / recall / F1 over character spans, matched by overlap within
+  a document.
+- **Entity accuracy** — over matched spans whose ids are comparable. The linker predicts
+  minted cluster ids (`kb::C0007`) while gold files carry input KB ids (`PEL.000032`), so
+  `entity_accuracy` may be `null`: undefined, not zero.
+
+`--kb-validation` adds `kb_lemma_validation`: how often a mention's predicted entity agrees
+with the entity its own lemma resolves to in the KB — a consistency check, not end-task
+accuracy.
+
+**Human-verified gold set.** Canonical predicate vocabulary, LLM pre-annotation, annotator
+agreement, and reference baselines scored through one harness: see the
+[gold evaluation guide](https://growgraph.github.io/pelinker/user_guide/evaluation/) and
+`run/eval/`.
+
+## Development
+
+```bash
+uv run pre-commit install            # ruff check + format on commit
+uv run pre-commit run --all-files
+uv run pytest test                   # `heavy` tests skip when model weights are absent
+uv sync --extra docs && uv run mkdocs serve
+```
 
-## Container
-1. Build image: `docker buildx build -t gg/pelinker:<current_version> --ssh default=$SSH_AUTH_SOCK . 2>&1 | tee build.log`
-2. Run container: `docker run --name pelinker --env THR_SCORE=0.5 gg/pelinker:latest`
+Commit with `uv run git commit …` so the hooks run in the project environment.
diff --git a/data/README.md b/data/README.md
new file mode 100644
index 0000000..ea71fb4
--- /dev/null
+++ b/data/README.md
@@ -0,0 +1,158 @@
+# Property KB: where each file comes from
+
+The linker, the gold annotation and the scoring all read one property vocabulary (the KB).
+This page covers which files are sources, which are hand-curated, which are generated, and
+what to re-run after an edit.
+
+**Rule of thumb:** edit only the **curated** files. The pairs KB
+(`derived/properties.synthesis.2.pairs.csv`) is always *generated*, so never edit it by
+hand. Re-run `derive_inverse_pairs.py` after any curated edit.
+
+```mermaid
+---
+config:
+  flowchart:
+    curve: catmullRom
+---
+flowchart TD
+    subgraph RAW["raw/ — upstream sources"]
+        RO_OWL["ro.owl<br/>Relations Ontology"]
+        GOCAM["GO-CAMs.ttl.gz"]
+        GW["properties.csv<br/>GeneWays verbs"]
+    end
+
+    subgraph EXTRACT["extraction — run/preprocessing/"]
+        X_RO(["extract_properties_ro.py"])
+        X_GO(["extract_properties_go.py"])
+        MERGE(["merge_properties.py"])
+    end
+
+    RO_OWL --> X_RO --> RO_CSV["derived/properties.ro.csv<br/>+ inverse_entity_id, is_symmetric"]
+    GOCAM --> X_GO --> GO_CSV["derived/properties.go.csv"]
+    RO_CSV --> MERGE
+    GW --> MERGE
+    MERGE -.->|"draft only; hand-curated after"| SYN["derived/properties.synthesis.2.csv"]
+
+    subgraph CURATED["hand-curated — edit these"]
+        SYN
+        INV["derived/properties.synthesis.2.inverse.csv<br/>same rows + inverse_entity_id"]
+        REVIEW["curated/inverse_pairs.review.csv<br/>accept / reject per derived pair"]
+        SYMM["curated/symmetric.csv<br/>symmetric ids RO does not flag"]
+    end
+
+    SYN <-.->|"kept in lockstep"| INV
+
+    DERIVE(["derive_inverse_pairs.py"])
+    INV --> DERIVE
+    REVIEW -->|"--review-csv"| DERIVE
+    SYMM -->|"--symmetric-csv"| DERIVE
+    RO_CSV -->|"--ro-csv-path<br/>(is_symmetric)"| DERIVE
+
+    DERIVE --> PAIRS[["derived/properties.synthesis.2.pairs.csv<br/>GENERATED — do not edit"]]
+    DERIVE -.->|"only while pairs await a verdict"| PENDING["derived/…pairs.pending_review.csv"]
+    PENDING -.->|"fill verdicts, copy rows in"| REVIEW
+
+    subgraph USE["consumers"]
+        FIT(["pelinker-fit / embed_kb_corpus.py<br/>stage A: weak labels"])
+        ANN(["run/eval/annotate_llm.py<br/>gold pre-annotation"])
+        SAMPLE(["run/eval/sample_gold.py, audit_sample.py<br/>gold sampling + coverage"])
+        EVAL(["run/eval/run_baselines.py,<br/>gold_review_sheet.py, dataset_stats.py"])
+    end
+
+    PAIRS -->|"kb_path="| FIT
+    PAIRS -->|"--kb-csv-path"| ANN
+    PAIRS -->|"--kb-csv-path"| EVAL
+    PAIRS -->|"--kb-csv-path"| SAMPLE
+```
+
+## Files
+
+| File | Kind | What it holds |
+|---|---|---|
+| `raw/ro.owl`, `raw/GO-CAMs.ttl.gz`, `raw/properties.csv` | source | Upstream ontologies and the GeneWays verb list. Never edited. |
+| `derived/properties.ro.csv` | generated (`extract_properties_ro.py`) | Every RO object property, its declared `owl:inverseOf` and `is_symmetric`. A property is symmetric when RO declares `owl:SymmetricProperty` or defines it by the chain `inverse(P) ∘ P`. |
+| `derived/properties.synthesis.2.csv` | **curated** | The vocabulary: `entity_id,label,description,example`. `merge_properties.py` produced the first draft; the current file is hand-curated and the script does not reproduce it. Uses CRLF line endings, so keep them when editing. |
+| `derived/properties.synthesis.2.inverse.csv` | **curated** | The same rows in the same order, plus `inverse_entity_id` for declared RO inverses. It is the input to the pair derivation. Any row added, removed or relabelled in `synthesis.2.csv` must be changed here too. |
+| `curated/inverse_pairs.review.csv` | **curated** | Verdicts on converse pairs found by surface rules: `accept` keeps the pair; `reject` unlinks it, so each member becomes its own canonical form. Keyed on the unordered id pair. Rows with no verdict count as unreviewed. |
+| `curated/symmetric.csv` | **curated** | Extra symmetric ids for entries RO does not flag (GeneWays and PEL verbs). Added to RO's flags. The derivation refuses an id that is not in the KB. |
+| `curated/equivalences.csv` | **curated**, scoring only | Identities the KB implies but does not declare (`tier=kb_implied`), such as a converse minted for a verb later marked symmetric. Read by the scoring reference (`pelinker/eval/reference.py`) and the review sheet's agreement; never by the derivation or the fit, so it cannot change training labels. |
+| `derived/properties.synthesis.2.pairs.csv` | **generated** (`derive_inverse_pairs.py`) | The KB every consumer reads. Adds `inverse_entity_id`, `inverse_label`, `inverse_source`, `needs_review`, `is_symmetric`, `is_canonical`, `canonical_entity_id`. |
+| `derived/properties.synthesis.2.pairs.pending_review.csv` | generated | Pairs still awaiting a verdict. Deleted automatically once none are left. |
+| `derived/properties.synthesis.{0,1}.csv`, `*.diverse.csv`, `selected_verbial_props_0.csv` | legacy / derived variants | Older vocabulary versions and entity subsets. Not read by the gold pipeline. |
+| `ground_truth/`, `test/` | fixtures | Test and sample data. |
+
+## What the pair derivation does
+
+`run/preprocessing/derive_inverse_pairs.py` reads the four inputs in the diagram and, for
+every KB row:
+
+1. **Symmetry.** Sets `is_symmetric` from RO's flag or the curated list. A symmetric
+   relation reads the same from either end ("interacts with"), so it never gets a converse
+   partner.
+2. **Converse pairs.** Links each entry to its converse partner, recording the rule in
+   `inverse_source`:
+   - `ro`: a declared `owl:inverseOf`. Authoritative.
+   - `has_of`: `has X` ↔ `X of`.
+   - `passive_by`: `X-ed by` ↔ the active entry with the same verb lemma. Irregular
+     participles count ("bound by" ↔ "binds to").
+   - `passive_prep`: the same for other prepositions ("expressed in" ↔ "expresses").
+
+   The last three are proposals and stay `needs_review` until the review sheet gives a
+   verdict.
+3. **Canonical member.** Elects one canonical member per pair: the active surface form
+   ("regulates", not "regulated by"). Orientation then lives in a separate `direction`
+   field (`forward`, `inverse`, `symmetric`, `na`), so a passive mention has exactly one
+   encoding.
+
+## Who reads the pairs KB, and why
+
+- **Gold annotation** (`annotate_llm.py`) offers the model **canonical labels only**, with
+  symmetric ones marked. A converse-form answer is folded onto its canonical id with the
+  direction flipped.
+- **Scoring** (`run_baselines.py`, the harness) compares ids in canonical space on both
+  sides. A system that answers "regulated by" where gold says (`regulates`, inverse)
+  picked the right relation.
+- **Training, stage (A)** (`pelinker-fit kb_path=…pairs.csv`) labels each verb mention from
+  the dependency parse, one label per mention, chosen by voice: "X is activated by Y" →
+  `activated by`. Symmetric relations are never flipped. Pass the pairs KB rather than
+  `synthesis.2.csv`, because the plain file carries no `is_symmetric`.
+
+## Recipes
+
+**Add or rename a relation.** Edit the row in both `synthesis.2.csv` and
+`synthesis.2.inverse.csv`, keeping the same id and position, then run the derivation.
+Mint new ids above the highest existing `PEL.*`.
+
+**Review derived pairs.** Run the derivation. Copy the rows of `*.pending_review.csv` into
+`curated/inverse_pairs.review.csv`, fill `verdict` (`accept` / `reject`) and add a `note` on
+anything non-obvious. Then run the derivation again until nothing is pending.
+
+**Mark a relation symmetric.** Add `entity_id,label,note` to `curated/symmetric.csv`, then
+run the derivation. A symmetric entry loses any surface-rule pairing, so no reject verdict
+is needed for it.
+
+**Regenerate RO flags after updating `raw/ro.owl`.** `rdflib` is in the `preprocess`
+extra:
+
+```bash
+uv run --with 'rdflib>=7.0' python run/preprocessing/extract_properties_ro.py
+```
+
+**Run the derivation.** The defaults point at the files above:
+
+```bash
+uv run python run/preprocessing/derive_inverse_pairs.py
+```
+
+Check the log: `Needing review: 0` means the pairs KB is ready to annotate against.
+
+## What an edit invalidates
+
+Any change that alters the canonical label list also changes the text of the annotation
+prompt. That covers a label, a canonical member, a symmetric flag or a pair verdict.
+
+- **LLM annotation cache:** misses for every document, so re-run `annotate_llm.py`.
+  Existing `gold.*.json` files were built against the old KB.
+- **Embedding parquets** from stage (A): their weak labels and `direction` were assigned
+  against the old KB, so re-embed before re-selecting hyperparameters and fitting.
+- **Scores** are comparable only between runs built on the same pairs KB.
diff --git a/data/curated/equivalences.csv b/data/curated/equivalences.csv
new file mode 100644
index 0000000..67cc247
--- /dev/null
+++ b/data/curated/equivalences.csv
@@ -0,0 +1,3 @@
+entity_id,equivalent_to,tier,note
+PEL.000040,PEL.000002,kb_implied,"'associated with' was minted as the converse of 'associates', which the KB marks symmetric"
+PEL.000041,PEL.000003,kb_implied,"'bound by' was minted as the converse of 'binds to', which the KB marks symmetric"
diff --git a/data/curated/inverse_pairs.review.csv b/data/curated/inverse_pairs.review.csv
new file mode 100644
index 0000000..9515097
--- /dev/null
+++ b/data/curated/inverse_pairs.review.csv
@@ -0,0 +1,56 @@
+entity_id,label,inverse_entity_id,inverse_label,inverse_source,verdict,note
+BFO.0000055,realizes,BFO.0000054,realized in,passive_prep,accept,
+BFO.0000063,precedes,BFO.0000062,preceded by,passive_by,accept,
+PEL.000026,produces,RO.0003001,produced by,passive_by,accept,
+RO.0002090,immediately precedes,RO.0002087,immediately preceded by,passive_by,accept,
+RO.0002221,surrounds,RO.0002219,surrounded by,passive_by,accept,
+RO.0002253,has connecting branch,RO.0002252,connecting branch of,has_of,accept,
+RO.0002257,developmentally induces,RO.0002256,developmentally induced by,passive_by,accept,
+RO.0002292,expresses,RO.0002206,expressed in,passive_prep,accept,
+RO.0002293,ubiquitously expresses,RO.0002291,ubiquitously expressed in,passive_prep,accept,
+RO.0002333,enabled by,RO.0002327,enables,passive_by,accept,
+RO.0002334,regulated by,RO.0002211,regulates,passive_by,accept,
+RO.0002335,negatively regulated by,RO.0002212,negatively regulates,passive_by,accept,
+RO.0002336,positively regulated by,RO.0002213,positively regulates,passive_by,accept,
+RO.0002456,pollinated by,RO.0002455,pollinates,passive_by,accept,
+RO.0002470,eats,RO.0002471,is eaten by,passive_by,accept,
+RO.0002508,determines,RO.0002507,determined by,passive_by,accept,
+RO.0002566,causally influences,RO.0002559,causally influenced by,passive_by,accept,
+RO.0002569,has branching part,RO.0002380,branching part of,has_of,accept,
+RO.0002578,directly regulates,RO.0002022,directly regulated by,passive_by,accept,
+RO.0002619,visited by,RO.0002618,visits,passive_by,accept,
+RO.0002627,is killed by,RO.0002626,kills,passive_by,accept,
+RO.0017009,is owned by,RO.0017008,owns,passive_by,accept,
+PEL.000000,activates,PEL.000038,activated by,passive_by,accept,
+PEL.000001,actuates,PEL.000039,actuated by,passive_by,accept,
+PEL.000004,causes,PEL.000042,caused by,passive_by,accept,
+PEL.000005,constrains,PEL.000043,constrained by,passive_by,accept,
+PEL.000006,controls,PEL.000044,controlled by,passive_by,accept,
+PEL.000007,degrades,PEL.000045,degraded by,passive_by,accept,
+PEL.000008,destroys,PEL.000046,destroyed by,passive_by,accept,
+PEL.000009,directs,PEL.000047,directed by,passive_by,accept,
+PEL.000010,downregulates,PEL.000048,downregulated by,passive_by,accept,
+PEL.000011,enhances,PEL.000049,enhanced by,passive_by,accept,
+PEL.000012,facilitates,PEL.000050,facilitated by,passive_by,accept,
+PEL.000013,forces,PEL.000051,forced by,passive_by,accept,
+PEL.000014,hinders,PEL.000052,hindered by,passive_by,accept,
+PEL.000015,inactivates,PEL.000053,inactivated by,passive_by,accept,
+PEL.000016,increases,PEL.000054,increased by,passive_by,accept,
+PEL.000017,induces,PEL.000055,induced by,passive_by,accept,
+PEL.000018,inhibits,PEL.000056,inhibited by,passive_by,accept,
+PEL.000019,initiates,PEL.000057,initiated by,passive_by,accept,
+PEL.000022,limits,PEL.000059,limited by,passive_by,accept,
+PEL.000023,mentions,PEL.000060,mentioned by,passive_by,accept,"discourse relation, non-biomedical"
+PEL.000024,overproduces,PEL.000061,overproduced by,passive_by,accept,
+PEL.000025,phosphorylates,PEL.000062,phosphorylated by,passive_by,accept,
+PEL.000027,promotes,PEL.000063,promoted by,passive_by,accept,
+PEL.000028,provokes,PEL.000064,provoked by,passive_by,accept,
+PEL.000029,reduces,PEL.000065,reduced by,passive_by,accept,
+PEL.000030,represses,PEL.000066,repressed by,passive_by,accept,label fixed: 'repress' -> 'represses'
+PEL.000031,reverts,PEL.000067,reverted by,passive_by,accept,
+PEL.000032,secretes,PEL.000068,secreted by,passive_by,accept,
+PEL.000035,suppresses,PEL.000070,suppressed by,passive_by,accept,label fixed: 'suppress' -> 'suppresses'
+PEL.000036,triggers,PEL.000071,triggered by,passive_by,accept,
+PEL.000037,upregulates,PEL.000072,upregulated by,passive_by,accept,
+PEL.000020,interrupts,PEL.000058,interrupted by,passive_by,accept,
+PEL.000034,stimulates,PEL.000069,stimulated by,passive_by,accept,
diff --git a/data/curated/symmetric.csv b/data/curated/symmetric.csv
new file mode 100644
index 0000000..2533d25
--- /dev/null
+++ b/data/curated/symmetric.csv
@@ -0,0 +1,7 @@
+entity_id,label,note
+PEL.000002,associates,association is symmetric: A associated with B iff B associated with A
+PEL.000040,associated with,association is symmetric: A associated with B iff B associated with A
+PEL.000003,binds to,physical binding is symmetric
+PEL.000041,bound by,physical binding is symmetric
+RO.0002170,connected to,connected to is symmetric (RO chain inverse(connects) o connects); connects is an equivalent
+RO.0002176,connects
diff --git a/data/derived/properties.ro.csv b/data/derived/properties.ro.csv
index bcf7731..af236db 100644
--- a/data/derived/properties.ro.csv
+++ b/data/derived/properties.ro.csv
@@ -1,48 +1,48 @@
-entity_id,label,description
-BFO.0000050,part of,a core relation that holds between a part and its whole
-BFO.0000051,has part,a core relation that holds between a whole and its part
-BFO.0000054,realized in,
-BFO.0000055,realizes,
-BFO.0000060,obsolete preceded by,
-BFO.0000062,preceded by,"x is preceded by y if and only if the time point at which y ends is before or equivalent to the time point at which x starts. Formally: x preceded by y iff ω(y) <= α(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point."
-BFO.0000063,precedes,"x precedes y if and only if the time point at which x ends is before or equivalent to the time point at which y starts. Formally: x precedes y iff ω(x) <= α(y), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point."
-BFO.0000066,occurs in,b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t
-BFO.0000067,contains process,[copied from inverse property 'occurs in'] b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t
-ENVO.01003004,derives from part of,"A relation between two distinct material entities, the new entity and the old entity, in which the new entity begins to exist through the separation or transformation of a part of the old entity, and the new entity inherits a significant portion of the matter belonging to that part of the old entity."
-RO.0000052,characteristic of,"a relation between a specifically dependent continuant (the characteristic) and any other entity (the bearer), in which the characteristic depends on the bearer for its existence."
-RO.0000053,has characteristic,Inverse of characteristic_of
-RO.0000056,participates in,"a relation between a continuant and a process, in which the continuant is somehow involved in the process"
-RO.0000057,has participant,"a relation between a process and a continuant, in which the continuant is somehow involved in the process"
-RO.0000058,is concretized as,"A relationship between a generically dependent continuant and a specifically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. A generically dependent continuant may be concretized as multiple specifically dependent continuants."
-RO.0000059,concretizes,"A relationship between a specifically dependent continuant and a generically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. Multiple specifically dependent continuants can concretize the same generically dependent continuant."
-RO.0000079,function of,"a relation between a function and an independent continuant (the bearer), in which the function specifically depends on the bearer for its existence"
-RO.0000080,quality of,"a relation between a quality and an independent continuant (the bearer), in which the quality specifically depends on the bearer for its existence"
-RO.0000081,role of,"a relation between a role and an independent continuant (the bearer), in which the role specifically depends on the bearer for its existence"
-RO.0000085,has function,"a relation between an independent continuant (the bearer) and a function, in which the function specifically depends on the bearer for its existence"
-RO.0000086,has quality,"a relation between an independent continuant (the bearer) and a quality, in which the quality specifically depends on the bearer for its existence"
-RO.0000087,has role,"a relation between an independent continuant (the bearer) and a role, in which the role specifically depends on the bearer for its existence"
-RO.0000091,has disposition,"a relation between an independent continuant (the bearer) and a disposition, in which the disposition specifically depends on the bearer for its existence"
-RO.0000092,disposition of,inverse of has disposition
-RO.0000300,obsolete in neural circuit with,"OBSOLETE A relation that holds between two neurons connected directly via a synapse, or indirectly via a series of synaptically connected neurons."
-RO.0000301,obsolete upstream in neural circuit with,"OBSOLETE A relation that holds between a neuron that is synapsed_to another neuron or a neuron that is connected indirectly to another by a chain of neurons, each synapsed_to the next, in which the direction is from the last to the first."
-RO.0000302,obsolete downstream in neural circuit with,"OBSOLETE A relation that holds between a neuron that is synapsed_by another neuron or a neuron that is connected indirectly to another by a chain of neurons, each synapsed_by the next, in which the direction is from the last to the first."
-RO.0001000,derives from,"a relation between two distinct material entities, the new entity and the old entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity"
-RO.0001001,derives into,"a relation between two distinct material entities, the old entity and the new entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity"
-RO.0001015,location of,"a relation between two independent continuants, the location and the target, in which the target is entirely within the location"
-RO.0001018,obsolete contained in,
-RO.0001019,obsolete contains,
-RO.0001020,is allergic trigger for,"A relation between a material entity and a condition (a phenotype or disease) of a host, in which the material entity is not part of the host, and is considered harmless to non-allergic hosts, and the condition results in pathological processes that include an abnormally strong immune response against the material entity."
-RO.0001021,is autoimmune trigger for,"A relation between a material entity and a condition (a phenotype or disease) of a host, in which the material entity is part of the host itself, and the condition results in pathological processes that include an abnormally strong immune response against the material entity."
-RO.0001022,has allergic trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is not part of the host, and is considered harmless to non-allergic hosts, and the condition results in pathological processes that include an abnormally strong immune response against the material entity."
-RO.0001023,has autoimmune trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is part of the host itself, and the condition results in pathological processes that include an abnormally strong immune response against the material entity."
-RO.0001025,located in,"a relation between two independent continuants, the target and the location, in which the target is entirely within the location"
-RO.0002000,2D boundary of,"a relation between a 2D immaterial entity (the boundary) and a material entity, in which the boundary delimits the material entity"
-RO.0002001,aligned with,
-RO.0002002,has 2D boundary,"a relation between a material entity and a 2D immaterial entity (the boundary), in which the boundary delimits the material entity"
-RO.0002003,electrically_synapsed_to,A relation that holds between two neurons that are electrically coupled via gap junctions.
-RO.0002004,tracheates,The relationship that holds between a trachea or tracheole and an antomical structure that is contained in (and so provides an oxygen supply to).
-RO.0002005,innervated_by,
-RO.0002006,has synaptic terminal of,
+entity_id,label,description,inverse_entity_id,is_symmetric
+BFO.0000050,part of,a core relation that holds between a part and its whole,BFO.0000051,False
+BFO.0000051,has part,a core relation that holds between a whole and its part,,False
+BFO.0000054,realized in,,BFO.0000055,False
+BFO.0000055,realizes,,,False
+BFO.0000060,obsolete preceded by,,,False
+BFO.0000062,preceded by,"x is preceded by y if and only if the time point at which y ends is before or equivalent to the time point at which x starts. Formally: x preceded by y iff ω(y) <= α(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",BFO.0000063,False
+BFO.0000063,precedes,"x precedes y if and only if the time point at which x ends is before or equivalent to the time point at which y starts. Formally: x precedes y iff ω(x) <= α(y), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,False
+BFO.0000066,occurs in,b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t,BFO.0000067,False
+BFO.0000067,contains process,[copied from inverse property 'occurs in'] b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t,,False
+ENVO.01003004,derives from part of,"A relation between two distinct material entities, the new entity and the old entity, in which the new entity begins to exist through the separation or transformation of a part of the old entity, and the new entity inherits a significant portion of the matter belonging to that part of the old entity.",,False
+RO.0000052,characteristic of,"a relation between a specifically dependent continuant (the characteristic) and any other entity (the bearer), in which the characteristic depends on the bearer for its existence.",RO.0000053,False
+RO.0000053,has characteristic,Inverse of characteristic_of,,False
+RO.0000056,participates in,"a relation between a continuant and a process, in which the continuant is somehow involved in the process",RO.0000057,False
+RO.0000057,has participant,"a relation between a process and a continuant, in which the continuant is somehow involved in the process",,False
+RO.0000058,is concretized as,"A relationship between a generically dependent continuant and a specifically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. A generically dependent continuant may be concretized as multiple specifically dependent continuants.",RO.0000059,False
+RO.0000059,concretizes,"A relationship between a specifically dependent continuant and a generically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. Multiple specifically dependent continuants can concretize the same generically dependent continuant.",,False
+RO.0000079,function of,"a relation between a function and an independent continuant (the bearer), in which the function specifically depends on the bearer for its existence",RO.0000085,False
+RO.0000080,quality of,"a relation between a quality and an independent continuant (the bearer), in which the quality specifically depends on the bearer for its existence",RO.0000086,False
+RO.0000081,role of,"a relation between a role and an independent continuant (the bearer), in which the role specifically depends on the bearer for its existence",RO.0000087,False
+RO.0000085,has function,"a relation between an independent continuant (the bearer) and a function, in which the function specifically depends on the bearer for its existence",,False
+RO.0000086,has quality,"a relation between an independent continuant (the bearer) and a quality, in which the quality specifically depends on the bearer for its existence",,False
+RO.0000087,has role,"a relation between an independent continuant (the bearer) and a role, in which the role specifically depends on the bearer for its existence",,False
+RO.0000091,has disposition,"a relation between an independent continuant (the bearer) and a disposition, in which the disposition specifically depends on the bearer for its existence",RO.0000092,False
+RO.0000092,disposition of,inverse of has disposition,,False
+RO.0000300,obsolete in neural circuit with,"OBSOLETE A relation that holds between two neurons connected directly via a synapse, or indirectly via a series of synaptically connected neurons.",,False
+RO.0000301,obsolete upstream in neural circuit with,"OBSOLETE A relation that holds between a neuron that is synapsed_to another neuron or a neuron that is connected indirectly to another by a chain of neurons, each synapsed_to the next, in which the direction is from the last to the first.",,False
+RO.0000302,obsolete downstream in neural circuit with,"OBSOLETE A relation that holds between a neuron that is synapsed_by another neuron or a neuron that is connected indirectly to another by a chain of neurons, each synapsed_by the next, in which the direction is from the last to the first.",,False
+RO.0001000,derives from,"a relation between two distinct material entities, the new entity and the old entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity",RO.0001001,False
+RO.0001001,derives into,"a relation between two distinct material entities, the old entity and the new entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity",,False
+RO.0001015,location of,"a relation between two independent continuants, the location and the target, in which the target is entirely within the location",RO.0001025,False
+RO.0001018,obsolete contained in,,,False
+RO.0001019,obsolete contains,,,False
+RO.0001020,is allergic trigger for,"A relation between a material entity and a condition (a phenotype or disease) of a host, in which the material entity is not part of the host, and is considered harmless to non-allergic hosts, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",RO.0001022,False
+RO.0001021,is autoimmune trigger for,"A relation between a material entity and a condition (a phenotype or disease) of a host, in which the material entity is part of the host itself, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",RO.0001023,False
+RO.0001022,has allergic trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is not part of the host, and is considered harmless to non-allergic hosts, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",,False
+RO.0001023,has autoimmune trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is part of the host itself, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",,False
+RO.0001025,located in,"a relation between two independent continuants, the target and the location, in which the target is entirely within the location",,False
+RO.0002000,2D boundary of,"a relation between a 2D immaterial entity (the boundary) and a material entity, in which the boundary delimits the material entity",RO.0002002,False
+RO.0002001,aligned with,,,False
+RO.0002002,has 2D boundary,"a relation between a material entity and a 2D immaterial entity (the boundary), in which the boundary delimits the material entity",,False
+RO.0002003,electrically_synapsed_to,A relation that holds between two neurons that are electrically coupled via gap junctions.,,False
+RO.0002004,tracheates,The relationship that holds between a trachea or tracheole and an antomical structure that is contained in (and so provides an oxygen supply to).,,False
+RO.0002005,innervated_by,,RO.0002134,False
+RO.0002006,has synaptic terminal of,,RO.0002130,False
 RO.0002007,bounding layer of,"X outer_layer_of Y iff:
 . X :continuant that bearer_of some PATO:laminar
 . X part_of Y
@@ -51,25 +51,25 @@ RO.0002007,bounding layer of,"X outer_layer_of Y iff:
 . Z boundary_of Y
 
 has_boundary: http://purl.obolibrary.org/obo/RO_0002002
-boundary_of: http://purl.obolibrary.org/obo/RO_0002000"
-RO.0002008,coincident with,A relation that holds between two linear structures that are approximately parallel to each other for their entire length and where either the two structures are adjacent to each other or one is part of the other.
-RO.0002009,cell expresses,"A relation that applies between a cell(c) and a gene(g) , where the process of 'transcription, DNA templated (GO_0006351)' is occuring in in cell c and that process has input gene g."
-RO.0002010,regulates in another organism,"x 'regulates in other organism' y if and only if: (x is the realization of a function to exert an effect on the frequency, rate or extent of y) AND (the agents of x are produced by organism o1 and the agents of y are produced by organism o2)."
-RO.0002011,regulates transport of,A relationship that holds between a  process that regulates a transport process and the entity transported by that process.
-RO.0002012,occurrent part of,A part of relation that applies only between occurrents.
-RO.0002013,has regulatory component activity,"A 'has regulatory component activity' B if A and B are GO molecular functions (GO_0003674), A has_component B and A is regulated by B."
-RO.0002014,has negative regulatory component activity,"A relationship that holds between a GO molecular function and a component of that molecular function that negatively regulates the activity of the whole.  More formally, A 'has regulatory component activity' B iff :A and B are GO molecular functions (GO_0003674), A has_component B and A is negatively regulated by B."
-RO.0002015,has positive regulatory component activity,"A relationship that holds between a GO molecular function and a component of that molecular function that positively regulates the activity of the whole.  More formally, A 'has regulatory component activity' B iff :A and B are GO molecular functions (GO_0003674), A has_component B and A is positively regulated by B."
-RO.0002016,has necessary component activity,
-RO.0002017,has component activity,
-RO.0002018,has component process,"w 'has process component' p if p and w are processes,  w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type."
-RO.0002019,has ligand,"A relationship that holds between between a receptor and an chemical entity, typically a small molecule or peptide, that carries information between cells or compartments of a cell and which binds the receptor and regulates its effector function."
-RO.0002020,transports,Holds between p and c when p is a transport process or transporter activity and the outcome of this p is to move c from one location to another.
-RO.0002021,occurs across,"A relationship between a process and a barrier, where the process occurs in a region spanning the barrier. For cellular processes the barrier is typically a membrane. Examples include transport across a membrane and membrane depolarization."
-RO.0002022,directly regulated by,
-RO.0002023,directly negatively regulated by,"Process(P2) is directly negatively regulated by process(P1) iff: P1 negatively regulates P2 via direct physical interaction between an agent executing P1 (or some part of P1) and an agent executing P2 (or some part of P2).  For example, if protein A has protein binding activity(P1) that targets protein B and this binding  negatively regulates the kinase activity (P2) of protein B then P2 directly negatively regulated by P1."
-RO.0002024,directly positively regulated by,"Process(P2) is directly postively regulated by process(P1) iff: P1 positively regulates P2 via direct physical interaction between an agent executing P1 (or some part of P1) and an agent executing P2 (or some part of P2).  For example, if protein A has protein binding activity(P1) that targets protein B and this binding positively regulates the kinase activity (P2) of protein B then P2 is directly postively regulated by P1."
-RO.0002025,has effector activity,"A 'has effector activity' B if A and B are GO molecular functions (GO_0003674),  A 'has component activity' B and B is the effector (output function) of B.  Each compound function has only one effector activity."
+boundary_of: http://purl.obolibrary.org/obo/RO_0002000",,False
+RO.0002008,coincident with,A relation that holds between two linear structures that are approximately parallel to each other for their entire length and where either the two structures are adjacent to each other or one is part of the other.,,False
+RO.0002009,cell expresses,"A relation that applies between a cell(c) and a gene(g) , where the process of 'transcription, DNA templated (GO_0006351)' is occuring in in cell c and that process has input gene g.",,False
+RO.0002010,regulates in another organism,"x 'regulates in other organism' y if and only if: (x is the realization of a function to exert an effect on the frequency, rate or extent of y) AND (the agents of x are produced by organism o1 and the agents of y are produced by organism o2).",,False
+RO.0002011,regulates transport of,A relationship that holds between a  process that regulates a transport process and the entity transported by that process.,,False
+RO.0002012,occurrent part of,A part of relation that applies only between occurrents.,,False
+RO.0002013,has regulatory component activity,"A 'has regulatory component activity' B if A and B are GO molecular functions (GO_0003674), A has_component B and A is regulated by B.",,False
+RO.0002014,has negative regulatory component activity,"A relationship that holds between a GO molecular function and a component of that molecular function that negatively regulates the activity of the whole.  More formally, A 'has regulatory component activity' B iff :A and B are GO molecular functions (GO_0003674), A has_component B and A is negatively regulated by B.",,False
+RO.0002015,has positive regulatory component activity,"A relationship that holds between a GO molecular function and a component of that molecular function that positively regulates the activity of the whole.  More formally, A 'has regulatory component activity' B iff :A and B are GO molecular functions (GO_0003674), A has_component B and A is positively regulated by B.",,False
+RO.0002016,has necessary component activity,,,False
+RO.0002017,has component activity,,,False
+RO.0002018,has component process,"w 'has process component' p if p and w are processes,  w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type.",,False
+RO.0002019,has ligand,"A relationship that holds between between a receptor and an chemical entity, typically a small molecule or peptide, that carries information between cells or compartments of a cell and which binds the receptor and regulates its effector function.",,False
+RO.0002020,transports,Holds between p and c when p is a transport process or transporter activity and the outcome of this p is to move c from one location to another.,,False
+RO.0002021,occurs across,"A relationship between a process and a barrier, where the process occurs in a region spanning the barrier. For cellular processes the barrier is typically a membrane. Examples include transport across a membrane and membrane depolarization.",,False
+RO.0002022,directly regulated by,,RO.0002578,False
+RO.0002023,directly negatively regulated by,"Process(P2) is directly negatively regulated by process(P1) iff: P1 negatively regulates P2 via direct physical interaction between an agent executing P1 (or some part of P1) and an agent executing P2 (or some part of P2).  For example, if protein A has protein binding activity(P1) that targets protein B and this binding  negatively regulates the kinase activity (P2) of protein B then P2 directly negatively regulated by P1.",RO.0002630,False
+RO.0002024,directly positively regulated by,"Process(P2) is directly postively regulated by process(P1) iff: P1 positively regulates P2 via direct physical interaction between an agent executing P1 (or some part of P1) and an agent executing P2 (or some part of P2).  For example, if protein A has protein binding activity(P1) that targets protein B and this binding positively regulates the kinase activity (P2) of protein B then P2 is directly postively regulated by P1.",RO.0002629,False
+RO.0002025,has effector activity,"A 'has effector activity' B if A and B are GO molecular functions (GO_0003674),  A 'has component activity' B and B is the effector (output function) of B.  Each compound function has only one effector activity.",,False
 RO.0002026,in register with,"A relationship that holds between two images, A and B, where:
 A depicts X;
 B depicts Y;
@@ -77,644 +77,644 @@ X and Y are both of type T'
 C is a 2 layer image consiting of layers A and B;
 A and B are aligned in C according to a shared co-ordinate framework so that common features of X and Y are co-incident with each other.
 Note: A and B may be 2D or 3D.
-Examples include: the relationship between two channels collected simultaneously from a confocal microscope; the relationship between an image dpeicting X and a painted annotation layer that delineates regions of X; the relationship between the tracing of a neuron on an EM stack and the co-ordinate space of the stack; the relationship between two separately collected images that have been brought into register via some image registration software."
-RO.0002081,before or simultaneous with,
-RO.0002082,simultaneous with,"x simultaneous with y iff ω(x) = ω(y) and ω(α ) = ω(α), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point and '=' indicates the same instance in time."
-RO.0002083,before,
-RO.0002084,during which ends,
-RO.0002085,encompasses,
-RO.0002086,ends after,
-RO.0002087,immediately preceded by,
-RO.0002088,during which starts,
-RO.0002089,starts before,
-RO.0002090,immediately precedes,
-RO.0002091,starts during,
-RO.0002092,happens during,
-RO.0002093,ends during,
-RO.0002100,has soma location,Relation between a neuron and a material anatomical entity that its soma is part of.
+Examples include: the relationship between two channels collected simultaneously from a confocal microscope; the relationship between an image dpeicting X and a painted annotation layer that delineates regions of X; the relationship between the tracing of a neuron on an EM stack and the co-ordinate space of the stack; the relationship between two separately collected images that have been brought into register via some image registration software.",,False
+RO.0002081,before or simultaneous with,,,False
+RO.0002082,simultaneous with,"x simultaneous with y iff ω(x) = ω(y) and ω(α ) = ω(α), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point and '=' indicates the same instance in time.",,True
+RO.0002083,before,,,False
+RO.0002084,during which ends,,RO.0002093,False
+RO.0002085,encompasses,,RO.0002092,False
+RO.0002086,ends after,,,False
+RO.0002087,immediately preceded by,,RO.0002090,False
+RO.0002088,during which starts,,RO.0002091,False
+RO.0002089,starts before,,,False
+RO.0002090,immediately precedes,,,False
+RO.0002091,starts during,,,False
+RO.0002092,happens during,,,False
+RO.0002093,ends during,,,False
+RO.0002100,has soma location,Relation between a neuron and a material anatomical entity that its soma is part of.,,False
 RO.0002101,fasciculates with,"relationship between a neuron and a neuron projection bundle (e.g.- tract or nerve bundle) that one or more of its projections travels through.
-"
-RO.0002102,axon synapses in,Relation between a neuron and some structure its axon forms (chemical) synapses in.
-RO.0002103,synapsed by,Relation between an anatomical structure (including cells) and a neuron that chemically synapses to it. 
-RO.0002104,has plasma membrane part,"Holds between a cell c and a protein complex or protein p if and only if that cell has as part a plasma_membrane[GO:0005886], and that plasma membrane has p as part."
-RO.0002105,synapsed_via_type_Ib_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type Ib bouton.
-RO.0002106,synapsed_via_type_Is_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type Is bouton.
-RO.0002107,synapsed_via_type_II_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type II bouton.
-RO.0002108,synapsed_by_via_type_II_bouton,Relation between a muscle and a motor neuron that synapses to it via a type II bouton.
-RO.0002109,synapsed_by_via_type_Ib_bouton,Relation between a muscle and a motor neuron that synapses to it via a type Ib bouton.
-RO.0002110,has postsynaptic terminal in,Relation between a neuron and some structure (e.g.- a brain region) in which it receives (chemical) synaptic input. 
-RO.0002111,releases neurotransmitter,
-RO.0002112,synapsed_by_via_type_Is_bouton,Relation between a muscle and a motor neuron that synapses to it via a type Is bouton.
-RO.0002113,has presynaptic terminal in,Relation between a neuron and some structure (e.g.- a brain region) in which it receives (chemical) synaptic input.
-RO.0002114,synapsed_via_type_III_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type III bouton.
+",RO.0002132,False
+RO.0002102,axon synapses in,Relation between a neuron and some structure its axon forms (chemical) synapses in.,,False
+RO.0002103,synapsed by,Relation between an anatomical structure (including cells) and a neuron that chemically synapses to it. ,RO.0002120,False
+RO.0002104,has plasma membrane part,"Holds between a cell c and a protein complex or protein p if and only if that cell has as part a plasma_membrane[GO:0005886], and that plasma membrane has p as part.",,False
+RO.0002105,synapsed_via_type_Ib_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type Ib bouton.,RO.0002109,False
+RO.0002106,synapsed_via_type_Is_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type Is bouton.,RO.0002112,False
+RO.0002107,synapsed_via_type_II_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type II bouton.,RO.0002108,False
+RO.0002108,synapsed_by_via_type_II_bouton,Relation between a muscle and a motor neuron that synapses to it via a type II bouton.,,False
+RO.0002109,synapsed_by_via_type_Ib_bouton,Relation between a muscle and a motor neuron that synapses to it via a type Ib bouton.,,False
+RO.0002110,has postsynaptic terminal in,Relation between a neuron and some structure (e.g.- a brain region) in which it receives (chemical) synaptic input. ,RO.0002113,False
+RO.0002111,releases neurotransmitter,,,False
+RO.0002112,synapsed_by_via_type_Is_bouton,Relation between a muscle and a motor neuron that synapses to it via a type Is bouton.,,False
+RO.0002113,has presynaptic terminal in,Relation between a neuron and some structure (e.g.- a brain region) in which it receives (chemical) synaptic input.,,False
+RO.0002114,synapsed_via_type_III_bouton_to,A relation between a motor neuron and a muscle that it synapses to via a type III bouton.,RO.0002115,False
 RO.0002115,synapsed_by_via_type_III_bouton,"Relation between a muscle and a motor neuron that synapses to it via a type III bouton.
-"
+",,False
 RO.0002120,synapsed to," Relation between a neuron and an anatomical structure (including cells) that it chemically synapses to.
-        "
+        ",,False
 RO.0002121,dendrite synapsed in,"Relation between a neuron and some structure  (e.g.- a brain region) in which its dendrite receives synaptic input.
 
-	"
-RO.0002130,has synaptic terminal in,A general relation between a neuron and some structure in which it either chemically synapses to some target or in which it receives (chemical) synaptic input.
-RO.0002131,overlaps,x overlaps y if and only if there exists some z such that x has part z and z part of y
-RO.0002132,has fasciculating neuron projection,The relation between a neuron projection bundle and a neuron projection that is fasciculated with it.
+	",,False
+RO.0002130,has synaptic terminal in,A general relation between a neuron and some structure in which it either chemically synapses to some target or in which it receives (chemical) synaptic input.,,False
+RO.0002131,overlaps,x overlaps y if and only if there exists some z such that x has part z and z part of y,,True
+RO.0002132,has fasciculating neuron projection,The relation between a neuron projection bundle and a neuron projection that is fasciculated with it.,,False
 RO.0002134,innervates,"Relation between a 'neuron projection bundle' and a region in which one or more of its component neuron projections either synapses to targets or receives synaptic input.
 T innervates some R
-Expands_to: T has_fasciculating_neuron_projection that synapse_in some R."
-RO.0002150,continuous with,X continuous_with Y if and only if X and Y share a fiat boundary.
-RO.0002151,partially overlaps,"x partially overlaps y iff there exists some z such that z is part of x and z is part of y, and it is also the case that neither x is part of y or y is part of x"
-RO.0002156,derived by descent from,d derived_by_descent_from a if d is specified by some genetic program that is sequence-inherited-from a genetic program that specifies a.
-RO.0002157,has derived by descendant,inverse of derived by descent from
-RO.0002158,shares ancestor with,two individual entities d1 and d2 stand in a shares_ancestor_with relation if and only if there exists some a such that d1 derived_by_descent_from a and d2 derived_by_descent_from a.
-RO.0002159,serially homologous to,
-RO.0002160,only in taxon,"x only in taxon y if and only if x is in taxon y, and there is no other organism z such that y!=z a and x is in taxon z."
-RO.0002162,in taxon,"x is in taxon y if an only if y is an organism, and the relationship between x and y is one of: part of (reflexive), developmentally preceded by, derives from, secreted by, expressed."
-RO.0002163,spatially disjoint from,A is spatially_disjoint_from B if and only if they have no parts in common
-RO.0002170,connected to,"a is connected to b if and only if a and b are discrete structure, and there exists some connecting structure c, such that c connects a and b"
-RO.0002176,connects,"c connects a if and only if there exist some b such that a and b are similar parts of the same system, and c connects b, specifically, c connects a with b. When one structure connects two others it unites some aspect of the function or role they play within the system."
-RO.0002177,attached to part of,"a is attached to part of b if a is attached to b, or a is attached to some p, where p is part of b."
-RO.0002178,supplies,"Relation between an arterial structure and another structure, where the arterial structure acts as a conduit channeling fluid, substance or energy."
-RO.0002179,drains,"Relation between an collecting structure and another structure, where the collecting structure acts as a conduit channeling fluid, substance or energy away from the other structure."
-RO.0002180,has component,"w 'has component' p if w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type."
-RO.0002200,has phenotype,"A relationship that holds between a biological entity and a phenotype. Here a phenotype is construed broadly as any kind of quality of an organism part, a collection of these qualities, or a change in quality or qualities (e.g. abnormally increased temperature). The subject of this relationship can be an organism (where the organism has the phenotype, i.e. the qualities inhere in parts of this organism), a genomic entity such as a gene or genotype (if modifications of the gene or the genotype causes the phenotype), or a condition such as a disease (such that if the condition inheres in an organism, then the organism has the phenotype)."
-RO.0002201,phenotype of,inverse of has phenotype
-RO.0002202,develops from,x develops from y if and only if either (a) x directly develops from y or (b) there exists some z such that x directly develops from z and z develops from y
-RO.0002203,develops into,inverse of develops from
-RO.0002204,gene product of,"definition ""x has gene product of y if and only if y is a gene (SO:0000704) that participates in some gene expression process (GO:0010467) where the output of that process is either y or something that is ribosomally translated from x"""
-RO.0002205,has gene product,x has gene product y if and only if x is a gene (SO:0000704) that participates in some gene expression process (GO:0010467) where the output of that process is either y or something that is ribosomally translated from y
-RO.0002206,expressed in,"x expressed in y if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and the transcription of x is part of the gene expression process (iii) x is a mature gene product such as a protein, and x was translated or otherwise processes from a transcript that was transcribed as part of this gene expression process"
-RO.0002207,directly develops from,"Candidate definition: x directly_develops from y if and only if there exists some developmental process (GO:0032502) p such that x and y both participate in p, and x is the output of p and y is the input of p, and a substantial portion of the matter of x comes from y, and the start of x is coincident with or after the end of y."
-RO.0002208,parasitoid of,A parasite that kills or sterilizes its host
-RO.0002209,has parasitoid,inverse of parasitoid of
-RO.0002210,directly develops into,inverse of directly develops from
-RO.0002211,regulates,"p regulates q iff p is causally upstream of q, the execution of p is not constant and varies according to specific conditions, and p influences the rate or magnitude of execution of q due to an effect either on some enabler of q or some enabler of a part of q."
-RO.0002212,negatively regulates,"p negatively regulates q iff p regulates q, and p decreases the rate or magnitude of execution of q."
-RO.0002213,positively regulates,"p positively regulates q iff p regulates q, and p increases the rate or magnitude of execution of q."
-RO.0002214,has prototype,"x has prototype y if and only if x is an instance of C and y is a prototypical instance of C. For example, every instance of heart, both normal and abnormal is related by the has prototype relation to some instance of a ""canonical"" heart, which participates in blood circulation."
-RO.0002215,capable of,"A relation between a material entity (such as a cell) and a process, in which the material entity has the ability to carry out the process. "
-RO.0002216,capable of part of,"c stands in this relationship to p if and only if there exists some p' such that c is capable_of p', and p' is part_of p."
-RO.0002217,obsolete actively participates in,OBSOLETE x actively participates in y if and only if x participates in y and x realizes some active role
-RO.0002218,obsolete has active participant,OBSOLETE x has participant y if and only if x realizes some active role that inheres in y
-RO.0002219,surrounded by,"x surrounded_by y if and only if (1) x is adjacent to y and for every region r that is adjacent to x, r overlaps y (2) the shared boundary between x and y occupies the majority of the outermost boundary of x"
-RO.0002220,adjacent to,x adjacent to y if and only if x and y share a boundary.
-RO.0002221,surrounds,inverse of surrounded by
-RO.0002222,temporally related to,
-RO.0002223,starts,inverse of starts with
-RO.0002224,starts with,"x starts with y if and only if x has part y and the time point at which x starts is equivalent to the time point at which y starts. Formally: α(y) = α(x) ∧ ω(y) < ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point."
-RO.0002225,develops from part of,x develops from part of y if and only if there exists some z such that x develops from z and z is part of y
-RO.0002226,develops in,x develops_in y if x is located in y whilst x is developing
-RO.0002227,obligate parasite of,A sub-relation of parasite-of in which the parasite that cannot complete its life cycle without a host.
-RO.0002228,facultative parasite of,A sub-relations of parasite-of in which the parasite that can complete its life cycle independent of a host.
-RO.0002229,ends,inverse of ends with
-RO.0002230,ends with,"x ends with y if and only if x has part y and the time point at which x ends is equivalent to the time point at which y ends. Formally: α(y) > α(x) ∧ ω(y) = ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point."
-RO.0002231,has start location,x 'has starts location' y if and only if there exists some process z such that x 'starts with' z and z 'occurs in' y
-RO.0002232,has end location,x 'has end location' y if and only if there exists some process z such that x 'ends with' z and z 'occurs in' y
-RO.0002233,has input,"p has input c iff: p is a process, c is a material entity, c is a participant in p, c is present at the start of p, and the state of c is modified during p."
-RO.0002234,has output,"p has output c iff c is a participant in p, c is present at the end of p, and c is not present in the same state at the beginning of p."
-RO.0002235,stem parasite of,A parasite-of relationship in which the host is a plant and the parasite that attaches to the host stem (PO:0009047)
-RO.0002236,root parasite of,A parasite-of relationship in which the host is a plant and the parasite that attaches to the host root (PO:0009005)
-RO.0002237,hemiparasite of,"A sub-relation of parasite-of in which the parasite is a plant, and the parasite is parasitic under natural conditions and is also photosynthetic to some degree. Hemiparasites may just obtain water and mineral nutrients from the host plant. Many obtain at least part of their organic nutrients from the host as well."
-RO.0002238,has component participant,X 'has component participant' Y means X 'has participant' Y and there is a cardinality constraint that specifies the numbers of Ys.
-RO.0002240,has exposure receptor,"A broad relationship between an exposure event or process and any entity (e.g., an organism, organism population, or an organism part) that interacts with an exposure stimulus during the exposure event."
-RO.0002241,has exposure stressor,"A broad relationship between an exposure event or process and any agent, stimulus, activity, or event that causes stress or tension on an organism and interacts with an exposure receptor during an exposure event."
-RO.0002242,has exposure route,A broad relationship between an exposure event or process and a process by which the exposure stressor comes into contact with the exposure receptor
-RO.0002243,has exposure transport path,A broad relationship between an exposure event or process and the course takes from the source to the target.
-RO.0002244,related via exposure to,Any relationship between an exposure event or process and any other entity.
-RO.0002245,over-expressed in,"g is over-expressed in t iff g is expressed in t, and the expression level of g is increased relative to some background."
-RO.0002246,under-expressed in,"g is under-expressed in t iff g is expressed in t, and the expression level of g is decreased relative to some background."
-RO.0002248,has active ingredient,"A relationship that holds between a substance and a chemical entity, if the chemical entity is part of the substance, and the chemical entity forms the biologically active component of the substance."
-RO.0002249,active ingredient in,inverse of has active ingredient
-RO.0002252,connecting branch of,"b connecting-branch-of s iff b is connected to s, and there exists some tree-like structure t such that the mereological sum of b plus s is either the same as t or a branching-part-of t."
-RO.0002253,has connecting branch,inverse of connecting branch of
-RO.0002254,has developmental contribution from,x has developmental contribution from y iff x has some part z such that z develops from y
-RO.0002255,developmentally contributes to,inverse of has developmental contribution from
-RO.0002256,developmentally induced by,"t1 induced_by t2 if there is a process of developmental induction (GO:0031128) with t1 and t2 as interacting participants. t2 causes t1 to change its fate from a precursor material anatomical entity type T to T', where T' develops_from T"
-RO.0002257,developmentally induces,Inverse of developmentally induced by
-RO.0002258,developmentally preceded by,"Candidate definition: x developmentally related to y if and only if there exists some developmental process (GO:0032502) p such that x and y both participates in p, and x is the output of p and y is the input of p"
-RO.0002260,has biological role,c has-biological-role r iff c has-role r and r is a biological role (CHEBI:24432)
-RO.0002261,has application role,c has-application-role r iff c has-role r and r is an application role (CHEBI:33232)
-RO.0002262,has chemical role,c has-chemical-role r iff c has-role r and r is a chemical role (CHEBI:51086)
-RO.0002263,acts upstream of,"c acts upstream of p if and only if c enables some f that is involved in p' and p' occurs chronologically before p, is not part of p, and affects the execution of p. c is a material entity and f, p, p' are processes."
-RO.0002264,acts upstream of or within,"c acts upstream of or within p if c is enables f, and f is causally upstream of or within p. c is a material entity and p is an process."
-RO.0002285,developmentally replaces,"x developmentally replaces y if and only if there is some developmental process that causes x to move or to cease to exist, and for the site that was occupied by x to become occupied by y, where y either comes into existence in this site or moves to this site from somewhere else"
-RO.0002286,developmentally succeeded by,Inverse of developmentally preceded by
-RO.0002287,part of developmental precursor of,
-RO.0002291,ubiquitously expressed in,"x is ubiquitously expressed in y if and only if x is expressed in y, and the majority of cells in y express x"
-RO.0002292,expresses,"y expresses x if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and x was transcribed from a gene as part of the gene expression process (iii) x is a mature gene product (protein or RNA), and x was translated or otherwise processed from a transcript that was transcribed as part of the gene expression process."
-RO.0002293,ubiquitously expresses,inverse of ubiquiotously expressed in
-RO.0002295,results in developmental progression of,"p results in the developmental progression of s iff p is a developmental process and s is an anatomical entity and p causes s to undergo a change in state at some point along its natural developmental cycle (this cycle starts with its formation, through the mature structure, and ends with its loss)."
-RO.0002296,results in development of,p 'results in development of' c if and only if p is a developmental process and p results in the state of c changing from its initial state as a primordium or anlage through its mature state and to its final state.
-RO.0002297,results in formation of anatomical entity,
-RO.0002298,results in morphogenesis of,The relationship that links an entity with the process that results in the formation and shaping of that entity over time from an immature to a mature state.
-RO.0002299,results in maturation of,The relationship that links an entity with a process that results in the progression of the entity over time that is independent of changes in it's shape and results in an end point state of that entity.
-RO.0002300,results in disappearance of,
-RO.0002301,results in developmental regression of,
-RO.0002302,is treated by substance,Inverse of 'is substance that treats'
-RO.0002303,has habitat,"x 'has habitat' y if and only if: x is an organism, y is a habitat, and y can sustain and allow the growth of a population of xs."
-RO.0002304,"causally upstream of, positive effect","p is causally upstream of, positive effect q iff p is casually upstream of q, and the execution of p is required for the execution of q."
-RO.0002305,"causally upstream of, negative effect","p is causally upstream of, negative effect q iff p is casually upstream of q, and the execution of p decreases the execution of q."
-RO.0002309,has exposure stimulus,"A relationship between an exposure event or process and any agent, stimulus, activity, or event that causally effects an organism and interacts with an exposure receptor during an exposure event."
-RO.0002312,evolutionary variant of,
-RO.0002313,transports or maintains localization of,Holds between p and c when p is a localization process (localization covers maintenance of localization as well as its establishment)  and the outcome of this process is to regulate the localization of c.
-RO.0002314,characteristic of part of,q characteristic of part of w if and only if there exists some p such that q inheres in p and p part of w.
-RO.0002315,results in acquisition of features of,The relationship that links a specified entity with the process that results in an unspecified entity acquiring the features and characteristics of the specified entity
-RO.0002320,evolutionarily related to,A relationship that holds via some environmental process
-RO.0002321,ecologically related to,A relationship that is mediated in some way by the environment or environmental feature (ENVO:00002297)
-RO.0002322,confers advantage in,
-RO.0002323,mereotopologically related to,A mereological relationship or a topological relationship
-RO.0002324,developmentally related to,A relationship that holds between entities participating in some developmental process (GO:0032502)
-RO.0002325,colocalizes with,a colocalizes_with b if and only if  a is transiently or peripherally associated with b[GO].
-RO.0002326,contributes to,
-RO.0002327,enables,c enables p iff c is capable of p and c acts to execute p.
-RO.0002328,functionally related to,"A grouping relationship for any relationship directly involving a function, or that holds because of a function of one of the related entities."
-RO.0002329,part of structure that is capable of,"this relation holds between c and p when c is part of some c', and c' is capable of p."
-RO.0002330,genomically related to,"holds between two entities when some genome-level process such as gene expression is involved. This includes transcriptional, spliceosomal events. These relations can be used between either macromolecule entities (such as regions of nucleic acid) or between their abstract informational counterparts."
-RO.0002331,involved in,"c involved_in p if and only if c enables some process p', and p' is part of p"
-RO.0002332,regulates levels of,p regulates levels of c if p regulates some amount (PATO:0000070) of c
-RO.0002333,enabled by,inverse of enables
-RO.0002334,regulated by,inverse of regulates
-RO.0002335,negatively regulated by,inverse of negatively regulates
-RO.0002336,positively regulated by,inverse of positively regulates
-RO.0002337,related via localization to,A relationship that holds via some process of localization
-RO.0002338,has target start location,This relationship holds between p and l when p is a transport or localization process in which the outcome is to move some cargo c from some initial location l to some destination.
-RO.0002339,has target end location,This relationship holds between p and l when p is a transport or localization process in which the outcome is to move some cargo c from a an initial location to some destination l.
-RO.0002340,imports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the start location of c is part of the region that surrounds s."
-RO.0002341,results in transport along,"Holds between p and l when p is a transportation or localization process and the outcome of this process is to move c from one location to another, and the route taken by c follows a path that is aligned_with l "
-RO.0002342,results in transport across,"Holds between p and m when p is a transportation or localization process and the outcome of this process is to move c from one location to another, and the route taken by c follows a path that crosses m."
-RO.0002343,results in growth of,
-RO.0002344,results in transport to from or in,
-RO.0002345,exports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the end location of c is part of the region that surrounds s."
-RO.0002348,results in commitment to,p 'results in commitment to' c if and only if p is a developmental process and c is a cell and p results in the state of c changing such that is can only develop into a single cell type.
-RO.0002349,results in determination of,"p 'results in determination of' c if and only if p is a developmental process and c is a cell and p results in the state of c changing to be determined. Once a cell becomes determined, it becomes committed to differentiate down a particular pathway regardless of its environment."
-RO.0002350,member of,is member of is a mereological relation between a item and a collection.
-RO.0002351,has member,has member is a mereological relation between a collection and an item.
-RO.0002352,input of,inverse of has input
-RO.0002353,output of,inverse of has output
-RO.0002354,formed as result of,
-RO.0002355,results in structural organization of,A relationship between a process and an anatomical entity such that the process contributes to the act of creating the structural organization of the anatomical entity.
-RO.0002356,results in specification of,"The relationship linking a cell and its participation in a process that results in the fate of the cell being specified. Once specification has taken place, a cell will be committed to differentiate down a specific pathway if left in its normal environment. "
-RO.0002357,results in developmental induction of,"p results in developmental induction of c if and only if p is a collection of cell-cell signaling processes that signal to a neighbouring tissue that is the precursor of the mature c, where the signaling results in the commitment to cell types necessary for the formation of c."
-RO.0002360,has dendrite location,
-RO.0002371,attached to,"a is attached to b if and only if a and b are discrete objects or object parts, and there are physical connections between a and b such that a force pulling a will move b, or a force pulling b will move a"
-RO.0002372,has muscle origin,"m has_muscle_origin s iff m is attached_to s, and it is the case that when m contracts, s does not move. The site of the origin tends to be more proximal and have greater mass than what the other end attaches to."
-RO.0002373,has muscle insertion,"m has_muscle_insertion s iff m is attaches_to s, and it is the case that when m contracts, s moves. Insertions are usually connections of muscle via tendon to bone."
-RO.0002374,derived from ancestral fusion of,"x has_fused_element y iff: there exists some z : x has_part z, z homologous_to y, and y is a distinct element, the boundary between x and z is largely fiat"
-RO.0002375,in branching relationship with,"A relationship that holds between two material entities in a system of connected structures, where the branching relationship holds based on properties of the connecting network."
-RO.0002376,tributary of,"x tributary_of y if and only if x a channel for the flow of a substance into y, where y is larger than x. If x and y are hydrographic features, then y is the main stem of a river, or a lake or bay, but not the sea or ocean. If x and y are anatomical, then y is a vein."
-RO.0002377,distributary of,"x distributary_of y if and only if x is capable of channeling the flow of a substance to y, where y channels less of the substance than x"
-RO.0002378,anabranch of,x anabranch_of y if x is a distributary of y (i.e. it channels a from a larger flow from y) and x ultimately channels the flow back into y.
-RO.0002379,spatially coextensive with,x spatially_coextensive_with y if and inly if x and y have the same location
-RO.0002380,branching part of,x is a branching part of y if and only if x is part of y and x is connected directly or indirectly to the main stem of y
-RO.0002381,main stem of,"x main_stem_of y if y is a branching structure and x is a channel that traces a linear path through y, such that x has higher capacity than any other such path."
-RO.0002382,proper distributary of,x proper_distributary_of y iff x distributary_of y and x does not flow back into y
-RO.0002383,proper tributary of,x proper_tributary_of y iff x tributary_of y and x does not originate from y
-RO.0002384,has developmental potential involving,"x has developmental potential involving y iff x is capable of a developmental process with output y. y may be the successor of x, or may be a different structure in the vicinity (as for example in the case of developmental induction)."
-RO.0002385,has potential to developmentally contribute to,x has potential to developmentrally contribute to y iff x developmentally contributes to y or x is capable of developmentally contributing to y
-RO.0002386,has potential to developmentally induce,x has potential to developmentally induce y iff x developmentally induces y or x is capable of developmentally inducing y
-RO.0002387,has potential to develop into,x has the potential to develop into y iff x develops into y or if x is capable of developing into y
-RO.0002388,has potential to directly develop into,x has potential to directly develop into y iff x directly develops into y or x is capable of directly developing into y
-RO.0002400,has direct input,"p has direct input c iff c is a participant in p, c is present at the start of p, and the state of c is modified during p."
-RO.0002401,obsolete has indirect input,
-RO.0002402,obsolete has direct output,"p has direct input c iff c is a participanti n p, c is present at the end of p, and c is not present at the beginning of c. "
-RO.0002403,obsolete has indirect output,
-RO.0002404,causally downstream of,inverse of upstream of
-RO.0002405,immediately causally downstream of,
-RO.0002406,obsolete directly activates,
-RO.0002407,indirectly positively regulates,p indirectly positively regulates q iff p is indirectly causally upstream of q and p positively regulates q.
-RO.0002408,obsolete directly inhibits,
-RO.0002409,indirectly negatively regulates,p indirectly negatively regulates q iff p is indirectly causally upstream of q and p negatively regulates q.
-RO.0002410,causally related to,"relation that links two events, processes, states, or objects such that one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly or wholly responsible for the effect, and the effect is partly or wholly dependent on the cause."
-RO.0002411,causally upstream of,"p is causally upstream of q iff p is causally related to q, the end of p precedes the end of q, and p is not an occurrent part of q."
-RO.0002412,immediately causally upstream of,"p is immediately causally upstream of q iff p is causally upstream of q, and the end of p is coincident with the beginning of q."
-RO.0002413,provides input for,"p provides input for q iff p is immediately causally upstream of q, and there exists some c such that p has_output c and q has_input c."
-RO.0002414,transitively provides input for,transitive form of directly_provides_input_for
-RO.0002418,causally upstream of or within,"p is 'causally upstream or within' q iff p is causally related to q, and the end of p precedes, or is coincident with, the end of q."
-RO.0002424,differs in,
-RO.0002425,differs in attribute of,
-RO.0002426,differs in attribute,
-RO.0002427,causally downstream of or within,inverse of causally upstream of or within
-RO.0002428,involved in regulation of,c involved in regulation of p if c is involved in some p' and p' regulates some p
-RO.0002429,involved in positive regulation of,c involved in regulation of p if c is involved in some p' and p' positively regulates some p
-RO.0002430,involved in negative regulation of,c involved in regulation of p if c is involved in some p' and p' negatively regulates some p
-RO.0002431,involved in or involved in regulation of,c involved in or regulates p if and only if either (i) c is involved in p or (ii) c is involved in regulation of p
-RO.0002432,is active in,"c executes activity in d if and only if c enables p and p occurs_in d.  Assuming no action at a distance by gene products, if a gene product enables (is capable of) a process that occurs in some structure, it must have at least some part in that structure."
-RO.0002433,contributes to morphology of,"p contributes to morphology of w if and only if a change in the morphology of p entails a change in the morphology of w. Examples: every skull contributes to morphology of the head which it is a part of. Counter-example: nuclei do not generally contribute to the morphology of the cell they are part of, as they are buffered by cytoplasm."
-RO.0002434,interacts with,A relationship that holds between two entities in which the processes executed by the two entities are causally connected.
-RO.0002435,genetically interacts with,
-RO.0002436,molecularly interacts with,An interaction relationship in which the two partners are molecular entities that directly physically interact with each other for example via a stable binding interaction or a brief interaction during which one modifies the other.
-RO.0002437,biotically interacts with,An interaction relationship in which at least one of the partners is an organism and the other is either an organism or an abiotic entity with which the organism interacts.
-RO.0002438,trophically interacts with,An interaction relationship in which the partners are related via a feeding relationship.
-RO.0002439,preys on,"An interaction relationship involving a predation process, where the subject kills the target in order to eat it or to feed to siblings, offspring or group members"
-RO.0002440,symbiotically interacts with,A biotic interaction in which the two organisms live together in more or less intimate association.
-RO.0002441,commensually interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which one benefits and the other is unaffected (GO).
-RO.0002442,mutualistically interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which both organisms benefit from each other (GO).
-RO.0002443,interacts with via parasite-host interaction,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which association is disadvantageous or destructive to one of the organisms (GO).
-RO.0002444,parasite of,A parasite-host relationship where an organism benefits at the expense of another.
-RO.0002445,parasitized by,Inverse of parasite of
-RO.0002446,participates in a abiotic-biotic interaction with,"A biotic interaction relationship in which one partner is an organism and the other partner is inorganic. For example, the relationship between a sponge and the substrate to which is it anchored."
-RO.0002447,phosphorylates,
+Expands_to: T has_fasciculating_neuron_projection that synapse_in some R.",,False
+RO.0002150,continuous with,X continuous_with Y if and only if X and Y share a fiat boundary.,,True
+RO.0002151,partially overlaps,"x partially overlaps y iff there exists some z such that z is part of x and z is part of y, and it is also the case that neither x is part of y or y is part of x",,False
+RO.0002156,derived by descent from,d derived_by_descent_from a if d is specified by some genetic program that is sequence-inherited-from a genetic program that specifies a.,RO.0002157,False
+RO.0002157,has derived by descendant,inverse of derived by descent from,,False
+RO.0002158,shares ancestor with,two individual entities d1 and d2 stand in a shares_ancestor_with relation if and only if there exists some a such that d1 derived_by_descent_from a and d2 derived_by_descent_from a.,,True
+RO.0002159,serially homologous to,,,True
+RO.0002160,only in taxon,"x only in taxon y if and only if x is in taxon y, and there is no other organism z such that y!=z a and x is in taxon z.",,False
+RO.0002162,in taxon,"x is in taxon y if an only if y is an organism, and the relationship between x and y is one of: part of (reflexive), developmentally preceded by, derives from, secreted by, expressed.",,False
+RO.0002163,spatially disjoint from,A is spatially_disjoint_from B if and only if they have no parts in common,,False
+RO.0002170,connected to,"a is connected to b if and only if a and b are discrete structure, and there exists some connecting structure c, such that c connects a and b",,True
+RO.0002176,connects,"c connects a if and only if there exist some b such that a and b are similar parts of the same system, and c connects b, specifically, c connects a with b. When one structure connects two others it unites some aspect of the function or role they play within the system.",,False
+RO.0002177,attached to part of,"a is attached to part of b if a is attached to b, or a is attached to some p, where p is part of b.",,False
+RO.0002178,supplies,"Relation between an arterial structure and another structure, where the arterial structure acts as a conduit channeling fluid, substance or energy.",,False
+RO.0002179,drains,"Relation between an collecting structure and another structure, where the collecting structure acts as a conduit channeling fluid, substance or energy away from the other structure.",,False
+RO.0002180,has component,"w 'has component' p if w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type.",,False
+RO.0002200,has phenotype,"A relationship that holds between a biological entity and a phenotype. Here a phenotype is construed broadly as any kind of quality of an organism part, a collection of these qualities, or a change in quality or qualities (e.g. abnormally increased temperature). The subject of this relationship can be an organism (where the organism has the phenotype, i.e. the qualities inhere in parts of this organism), a genomic entity such as a gene or genotype (if modifications of the gene or the genotype causes the phenotype), or a condition such as a disease (such that if the condition inheres in an organism, then the organism has the phenotype).",RO.0002201,False
+RO.0002201,phenotype of,inverse of has phenotype,,False
+RO.0002202,develops from,x develops from y if and only if either (a) x directly develops from y or (b) there exists some z such that x directly develops from z and z develops from y,RO.0002203,False
+RO.0002203,develops into,inverse of develops from,,False
+RO.0002204,gene product of,"definition ""x has gene product of y if and only if y is a gene (SO:0000704) that participates in some gene expression process (GO:0010467) where the output of that process is either y or something that is ribosomally translated from x""",RO.0002205,False
+RO.0002205,has gene product,x has gene product y if and only if x is a gene (SO:0000704) that participates in some gene expression process (GO:0010467) where the output of that process is either y or something that is ribosomally translated from y,,False
+RO.0002206,expressed in,"x expressed in y if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and the transcription of x is part of the gene expression process (iii) x is a mature gene product such as a protein, and x was translated or otherwise processes from a transcript that was transcribed as part of this gene expression process",RO.0002292,False
+RO.0002207,directly develops from,"Candidate definition: x directly_develops from y if and only if there exists some developmental process (GO:0032502) p such that x and y both participate in p, and x is the output of p and y is the input of p, and a substantial portion of the matter of x comes from y, and the start of x is coincident with or after the end of y.",RO.0002210,False
+RO.0002208,parasitoid of,A parasite that kills or sterilizes its host,RO.0002209,False
+RO.0002209,has parasitoid,inverse of parasitoid of,,False
+RO.0002210,directly develops into,inverse of directly develops from,,False
+RO.0002211,regulates,"p regulates q iff p is causally upstream of q, the execution of p is not constant and varies according to specific conditions, and p influences the rate or magnitude of execution of q due to an effect either on some enabler of q or some enabler of a part of q.",RO.0002334,False
+RO.0002212,negatively regulates,"p negatively regulates q iff p regulates q, and p decreases the rate or magnitude of execution of q.",RO.0002335,False
+RO.0002213,positively regulates,"p positively regulates q iff p regulates q, and p increases the rate or magnitude of execution of q.",RO.0002336,False
+RO.0002214,has prototype,"x has prototype y if and only if x is an instance of C and y is a prototypical instance of C. For example, every instance of heart, both normal and abnormal is related by the has prototype relation to some instance of a ""canonical"" heart, which participates in blood circulation.",,False
+RO.0002215,capable of,"A relation between a material entity (such as a cell) and a process, in which the material entity has the ability to carry out the process. ",,False
+RO.0002216,capable of part of,"c stands in this relationship to p if and only if there exists some p' such that c is capable_of p', and p' is part_of p.",,False
+RO.0002217,obsolete actively participates in,OBSOLETE x actively participates in y if and only if x participates in y and x realizes some active role,,False
+RO.0002218,obsolete has active participant,OBSOLETE x has participant y if and only if x realizes some active role that inheres in y,,False
+RO.0002219,surrounded by,"x surrounded_by y if and only if (1) x is adjacent to y and for every region r that is adjacent to x, r overlaps y (2) the shared boundary between x and y occupies the majority of the outermost boundary of x",RO.0002221,False
+RO.0002220,adjacent to,x adjacent to y if and only if x and y share a boundary.,,False
+RO.0002221,surrounds,inverse of surrounded by,,False
+RO.0002222,temporally related to,,,False
+RO.0002223,starts,inverse of starts with,RO.0002224,False
+RO.0002224,starts with,"x starts with y if and only if x has part y and the time point at which x starts is equivalent to the time point at which y starts. Formally: α(y) = α(x) ∧ ω(y) < ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,False
+RO.0002225,develops from part of,x develops from part of y if and only if there exists some z such that x develops from z and z is part of y,,False
+RO.0002226,develops in,x develops_in y if x is located in y whilst x is developing,,False
+RO.0002227,obligate parasite of,A sub-relation of parasite-of in which the parasite that cannot complete its life cycle without a host.,,False
+RO.0002228,facultative parasite of,A sub-relations of parasite-of in which the parasite that can complete its life cycle independent of a host.,,False
+RO.0002229,ends,inverse of ends with,RO.0002230,False
+RO.0002230,ends with,"x ends with y if and only if x has part y and the time point at which x ends is equivalent to the time point at which y ends. Formally: α(y) > α(x) ∧ ω(y) = ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,False
+RO.0002231,has start location,x 'has starts location' y if and only if there exists some process z such that x 'starts with' z and z 'occurs in' y,,False
+RO.0002232,has end location,x 'has end location' y if and only if there exists some process z such that x 'ends with' z and z 'occurs in' y,,False
+RO.0002233,has input,"p has input c iff: p is a process, c is a material entity, c is a participant in p, c is present at the start of p, and the state of c is modified during p.",RO.0002352,False
+RO.0002234,has output,"p has output c iff c is a participant in p, c is present at the end of p, and c is not present in the same state at the beginning of p.",RO.0002353,False
+RO.0002235,stem parasite of,A parasite-of relationship in which the host is a plant and the parasite that attaches to the host stem (PO:0009047),,False
+RO.0002236,root parasite of,A parasite-of relationship in which the host is a plant and the parasite that attaches to the host root (PO:0009005),,False
+RO.0002237,hemiparasite of,"A sub-relation of parasite-of in which the parasite is a plant, and the parasite is parasitic under natural conditions and is also photosynthetic to some degree. Hemiparasites may just obtain water and mineral nutrients from the host plant. Many obtain at least part of their organic nutrients from the host as well.",,False
+RO.0002238,has component participant,X 'has component participant' Y means X 'has participant' Y and there is a cardinality constraint that specifies the numbers of Ys.,,False
+RO.0002240,has exposure receptor,"A broad relationship between an exposure event or process and any entity (e.g., an organism, organism population, or an organism part) that interacts with an exposure stimulus during the exposure event.",,False
+RO.0002241,has exposure stressor,"A broad relationship between an exposure event or process and any agent, stimulus, activity, or event that causes stress or tension on an organism and interacts with an exposure receptor during an exposure event.",,False
+RO.0002242,has exposure route,A broad relationship between an exposure event or process and a process by which the exposure stressor comes into contact with the exposure receptor,,False
+RO.0002243,has exposure transport path,A broad relationship between an exposure event or process and the course takes from the source to the target.,,False
+RO.0002244,related via exposure to,Any relationship between an exposure event or process and any other entity.,,False
+RO.0002245,over-expressed in,"g is over-expressed in t iff g is expressed in t, and the expression level of g is increased relative to some background.",,False
+RO.0002246,under-expressed in,"g is under-expressed in t iff g is expressed in t, and the expression level of g is decreased relative to some background.",,False
+RO.0002248,has active ingredient,"A relationship that holds between a substance and a chemical entity, if the chemical entity is part of the substance, and the chemical entity forms the biologically active component of the substance.",RO.0002249,False
+RO.0002249,active ingredient in,inverse of has active ingredient,,False
+RO.0002252,connecting branch of,"b connecting-branch-of s iff b is connected to s, and there exists some tree-like structure t such that the mereological sum of b plus s is either the same as t or a branching-part-of t.",RO.0002253,False
+RO.0002253,has connecting branch,inverse of connecting branch of,,False
+RO.0002254,has developmental contribution from,x has developmental contribution from y iff x has some part z such that z develops from y,RO.0002255,False
+RO.0002255,developmentally contributes to,inverse of has developmental contribution from,,False
+RO.0002256,developmentally induced by,"t1 induced_by t2 if there is a process of developmental induction (GO:0031128) with t1 and t2 as interacting participants. t2 causes t1 to change its fate from a precursor material anatomical entity type T to T', where T' develops_from T",RO.0002257,False
+RO.0002257,developmentally induces,Inverse of developmentally induced by,,False
+RO.0002258,developmentally preceded by,"Candidate definition: x developmentally related to y if and only if there exists some developmental process (GO:0032502) p such that x and y both participates in p, and x is the output of p and y is the input of p",RO.0002286,False
+RO.0002260,has biological role,c has-biological-role r iff c has-role r and r is a biological role (CHEBI:24432),,False
+RO.0002261,has application role,c has-application-role r iff c has-role r and r is an application role (CHEBI:33232),,False
+RO.0002262,has chemical role,c has-chemical-role r iff c has-role r and r is a chemical role (CHEBI:51086),,False
+RO.0002263,acts upstream of,"c acts upstream of p if and only if c enables some f that is involved in p' and p' occurs chronologically before p, is not part of p, and affects the execution of p. c is a material entity and f, p, p' are processes.",,False
+RO.0002264,acts upstream of or within,"c acts upstream of or within p if c is enables f, and f is causally upstream of or within p. c is a material entity and p is an process.",,False
+RO.0002285,developmentally replaces,"x developmentally replaces y if and only if there is some developmental process that causes x to move or to cease to exist, and for the site that was occupied by x to become occupied by y, where y either comes into existence in this site or moves to this site from somewhere else",,False
+RO.0002286,developmentally succeeded by,Inverse of developmentally preceded by,,False
+RO.0002287,part of developmental precursor of,,,False
+RO.0002291,ubiquitously expressed in,"x is ubiquitously expressed in y if and only if x is expressed in y, and the majority of cells in y express x",RO.0002293,False
+RO.0002292,expresses,"y expresses x if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and x was transcribed from a gene as part of the gene expression process (iii) x is a mature gene product (protein or RNA), and x was translated or otherwise processed from a transcript that was transcribed as part of the gene expression process.",,False
+RO.0002293,ubiquitously expresses,inverse of ubiquiotously expressed in,,False
+RO.0002295,results in developmental progression of,"p results in the developmental progression of s iff p is a developmental process and s is an anatomical entity and p causes s to undergo a change in state at some point along its natural developmental cycle (this cycle starts with its formation, through the mature structure, and ends with its loss).",,False
+RO.0002296,results in development of,p 'results in development of' c if and only if p is a developmental process and p results in the state of c changing from its initial state as a primordium or anlage through its mature state and to its final state.,,False
+RO.0002297,results in formation of anatomical entity,,RO.0002354,False
+RO.0002298,results in morphogenesis of,The relationship that links an entity with the process that results in the formation and shaping of that entity over time from an immature to a mature state.,,False
+RO.0002299,results in maturation of,The relationship that links an entity with a process that results in the progression of the entity over time that is independent of changes in it's shape and results in an end point state of that entity.,,False
+RO.0002300,results in disappearance of,,,False
+RO.0002301,results in developmental regression of,,,False
+RO.0002302,is treated by substance,Inverse of 'is substance that treats',RO.0002606,False
+RO.0002303,has habitat,"x 'has habitat' y if and only if: x is an organism, y is a habitat, and y can sustain and allow the growth of a population of xs.",,False
+RO.0002304,"causally upstream of, positive effect","p is causally upstream of, positive effect q iff p is casually upstream of q, and the execution of p is required for the execution of q.",,False
+RO.0002305,"causally upstream of, negative effect","p is causally upstream of, negative effect q iff p is casually upstream of q, and the execution of p decreases the execution of q.",,False
+RO.0002309,has exposure stimulus,"A relationship between an exposure event or process and any agent, stimulus, activity, or event that causally effects an organism and interacts with an exposure receptor during an exposure event.",,False
+RO.0002312,evolutionary variant of,,,False
+RO.0002313,transports or maintains localization of,Holds between p and c when p is a localization process (localization covers maintenance of localization as well as its establishment)  and the outcome of this process is to regulate the localization of c.,,False
+RO.0002314,characteristic of part of,q characteristic of part of w if and only if there exists some p such that q inheres in p and p part of w.,,False
+RO.0002315,results in acquisition of features of,The relationship that links a specified entity with the process that results in an unspecified entity acquiring the features and characteristics of the specified entity,,False
+RO.0002320,evolutionarily related to,A relationship that holds via some environmental process,,False
+RO.0002321,ecologically related to,A relationship that is mediated in some way by the environment or environmental feature (ENVO:00002297),,False
+RO.0002322,confers advantage in,,,False
+RO.0002323,mereotopologically related to,A mereological relationship or a topological relationship,,False
+RO.0002324,developmentally related to,A relationship that holds between entities participating in some developmental process (GO:0032502),,False
+RO.0002325,colocalizes with,a colocalizes_with b if and only if  a is transiently or peripherally associated with b[GO].,,False
+RO.0002326,contributes to,,,False
+RO.0002327,enables,c enables p iff c is capable of p and c acts to execute p.,RO.0002333,False
+RO.0002328,functionally related to,"A grouping relationship for any relationship directly involving a function, or that holds because of a function of one of the related entities.",,False
+RO.0002329,part of structure that is capable of,"this relation holds between c and p when c is part of some c', and c' is capable of p.",,False
+RO.0002330,genomically related to,"holds between two entities when some genome-level process such as gene expression is involved. This includes transcriptional, spliceosomal events. These relations can be used between either macromolecule entities (such as regions of nucleic acid) or between their abstract informational counterparts.",,False
+RO.0002331,involved in,"c involved_in p if and only if c enables some process p', and p' is part of p",,False
+RO.0002332,regulates levels of,p regulates levels of c if p regulates some amount (PATO:0000070) of c,,False
+RO.0002333,enabled by,inverse of enables,,False
+RO.0002334,regulated by,inverse of regulates,,False
+RO.0002335,negatively regulated by,inverse of negatively regulates,,False
+RO.0002336,positively regulated by,inverse of positively regulates,,False
+RO.0002337,related via localization to,A relationship that holds via some process of localization,,False
+RO.0002338,has target start location,This relationship holds between p and l when p is a transport or localization process in which the outcome is to move some cargo c from some initial location l to some destination.,,False
+RO.0002339,has target end location,This relationship holds between p and l when p is a transport or localization process in which the outcome is to move some cargo c from a an initial location to some destination l.,,False
+RO.0002340,imports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the start location of c is part of the region that surrounds s.",,False
+RO.0002341,results in transport along,"Holds between p and l when p is a transportation or localization process and the outcome of this process is to move c from one location to another, and the route taken by c follows a path that is aligned_with l ",,False
+RO.0002342,results in transport across,"Holds between p and m when p is a transportation or localization process and the outcome of this process is to move c from one location to another, and the route taken by c follows a path that crosses m.",,False
+RO.0002343,results in growth of,,,False
+RO.0002344,results in transport to from or in,,,False
+RO.0002345,exports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the end location of c is part of the region that surrounds s.",,False
+RO.0002348,results in commitment to,p 'results in commitment to' c if and only if p is a developmental process and c is a cell and p results in the state of c changing such that is can only develop into a single cell type.,,False
+RO.0002349,results in determination of,"p 'results in determination of' c if and only if p is a developmental process and c is a cell and p results in the state of c changing to be determined. Once a cell becomes determined, it becomes committed to differentiate down a particular pathway regardless of its environment.",,False
+RO.0002350,member of,is member of is a mereological relation between a item and a collection.,RO.0002351,False
+RO.0002351,has member,has member is a mereological relation between a collection and an item.,,False
+RO.0002352,input of,inverse of has input,,False
+RO.0002353,output of,inverse of has output,,False
+RO.0002354,formed as result of,,,False
+RO.0002355,results in structural organization of,A relationship between a process and an anatomical entity such that the process contributes to the act of creating the structural organization of the anatomical entity.,,False
+RO.0002356,results in specification of,"The relationship linking a cell and its participation in a process that results in the fate of the cell being specified. Once specification has taken place, a cell will be committed to differentiate down a specific pathway if left in its normal environment. ",,False
+RO.0002357,results in developmental induction of,"p results in developmental induction of c if and only if p is a collection of cell-cell signaling processes that signal to a neighbouring tissue that is the precursor of the mature c, where the signaling results in the commitment to cell types necessary for the formation of c.",,False
+RO.0002360,has dendrite location,,,False
+RO.0002371,attached to,"a is attached to b if and only if a and b are discrete objects or object parts, and there are physical connections between a and b such that a force pulling a will move b, or a force pulling b will move a",,True
+RO.0002372,has muscle origin,"m has_muscle_origin s iff m is attached_to s, and it is the case that when m contracts, s does not move. The site of the origin tends to be more proximal and have greater mass than what the other end attaches to.",,False
+RO.0002373,has muscle insertion,"m has_muscle_insertion s iff m is attaches_to s, and it is the case that when m contracts, s moves. Insertions are usually connections of muscle via tendon to bone.",,False
+RO.0002374,derived from ancestral fusion of,"x has_fused_element y iff: there exists some z : x has_part z, z homologous_to y, and y is a distinct element, the boundary between x and z is largely fiat",,False
+RO.0002375,in branching relationship with,"A relationship that holds between two material entities in a system of connected structures, where the branching relationship holds based on properties of the connecting network.",,False
+RO.0002376,tributary of,"x tributary_of y if and only if x a channel for the flow of a substance into y, where y is larger than x. If x and y are hydrographic features, then y is the main stem of a river, or a lake or bay, but not the sea or ocean. If x and y are anatomical, then y is a vein.",,False
+RO.0002377,distributary of,"x distributary_of y if and only if x is capable of channeling the flow of a substance to y, where y channels less of the substance than x",,False
+RO.0002378,anabranch of,x anabranch_of y if x is a distributary of y (i.e. it channels a from a larger flow from y) and x ultimately channels the flow back into y.,,False
+RO.0002379,spatially coextensive with,x spatially_coextensive_with y if and inly if x and y have the same location,,False
+RO.0002380,branching part of,x is a branching part of y if and only if x is part of y and x is connected directly or indirectly to the main stem of y,RO.0002569,False
+RO.0002381,main stem of,"x main_stem_of y if y is a branching structure and x is a channel that traces a linear path through y, such that x has higher capacity than any other such path.",,False
+RO.0002382,proper distributary of,x proper_distributary_of y iff x distributary_of y and x does not flow back into y,,False
+RO.0002383,proper tributary of,x proper_tributary_of y iff x tributary_of y and x does not originate from y,,False
+RO.0002384,has developmental potential involving,"x has developmental potential involving y iff x is capable of a developmental process with output y. y may be the successor of x, or may be a different structure in the vicinity (as for example in the case of developmental induction).",,False
+RO.0002385,has potential to developmentally contribute to,x has potential to developmentrally contribute to y iff x developmentally contributes to y or x is capable of developmentally contributing to y,,False
+RO.0002386,has potential to developmentally induce,x has potential to developmentally induce y iff x developmentally induces y or x is capable of developmentally inducing y,,False
+RO.0002387,has potential to develop into,x has the potential to develop into y iff x develops into y or if x is capable of developing into y,,False
+RO.0002388,has potential to directly develop into,x has potential to directly develop into y iff x directly develops into y or x is capable of directly developing into y,,False
+RO.0002400,has direct input,"p has direct input c iff c is a participant in p, c is present at the start of p, and the state of c is modified during p.",,False
+RO.0002401,obsolete has indirect input,,,False
+RO.0002402,obsolete has direct output,"p has direct input c iff c is a participanti n p, c is present at the end of p, and c is not present at the beginning of c. ",,False
+RO.0002403,obsolete has indirect output,,,False
+RO.0002404,causally downstream of,inverse of upstream of,RO.0002411,False
+RO.0002405,immediately causally downstream of,,RO.0002412,False
+RO.0002406,obsolete directly activates,,,False
+RO.0002407,indirectly positively regulates,p indirectly positively regulates q iff p is indirectly causally upstream of q and p positively regulates q.,,False
+RO.0002408,obsolete directly inhibits,,,False
+RO.0002409,indirectly negatively regulates,p indirectly negatively regulates q iff p is indirectly causally upstream of q and p negatively regulates q.,,False
+RO.0002410,causally related to,"relation that links two events, processes, states, or objects such that one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly or wholly responsible for the effect, and the effect is partly or wholly dependent on the cause.",,False
+RO.0002411,causally upstream of,"p is causally upstream of q iff p is causally related to q, the end of p precedes the end of q, and p is not an occurrent part of q.",,False
+RO.0002412,immediately causally upstream of,"p is immediately causally upstream of q iff p is causally upstream of q, and the end of p is coincident with the beginning of q.",,False
+RO.0002413,provides input for,"p provides input for q iff p is immediately causally upstream of q, and there exists some c such that p has_output c and q has_input c.",,False
+RO.0002414,transitively provides input for,transitive form of directly_provides_input_for,,False
+RO.0002418,causally upstream of or within,"p is 'causally upstream or within' q iff p is causally related to q, and the end of p precedes, or is coincident with, the end of q.",RO.0002427,False
+RO.0002424,differs in,,,False
+RO.0002425,differs in attribute of,,,False
+RO.0002426,differs in attribute,,,False
+RO.0002427,causally downstream of or within,inverse of causally upstream of or within,,False
+RO.0002428,involved in regulation of,c involved in regulation of p if c is involved in some p' and p' regulates some p,,False
+RO.0002429,involved in positive regulation of,c involved in regulation of p if c is involved in some p' and p' positively regulates some p,,False
+RO.0002430,involved in negative regulation of,c involved in regulation of p if c is involved in some p' and p' negatively regulates some p,,False
+RO.0002431,involved in or involved in regulation of,c involved in or regulates p if and only if either (i) c is involved in p or (ii) c is involved in regulation of p,,False
+RO.0002432,is active in,"c executes activity in d if and only if c enables p and p occurs_in d.  Assuming no action at a distance by gene products, if a gene product enables (is capable of) a process that occurs in some structure, it must have at least some part in that structure.",,False
+RO.0002433,contributes to morphology of,"p contributes to morphology of w if and only if a change in the morphology of p entails a change in the morphology of w. Examples: every skull contributes to morphology of the head which it is a part of. Counter-example: nuclei do not generally contribute to the morphology of the cell they are part of, as they are buffered by cytoplasm.",,False
+RO.0002434,interacts with,A relationship that holds between two entities in which the processes executed by the two entities are causally connected.,,True
+RO.0002435,genetically interacts with,,,True
+RO.0002436,molecularly interacts with,An interaction relationship in which the two partners are molecular entities that directly physically interact with each other for example via a stable binding interaction or a brief interaction during which one modifies the other.,,True
+RO.0002437,biotically interacts with,An interaction relationship in which at least one of the partners is an organism and the other is either an organism or an abiotic entity with which the organism interacts.,,True
+RO.0002438,trophically interacts with,An interaction relationship in which the partners are related via a feeding relationship.,,False
+RO.0002439,preys on,"An interaction relationship involving a predation process, where the subject kills the target in order to eat it or to feed to siblings, offspring or group members",RO.0002458,False
+RO.0002440,symbiotically interacts with,A biotic interaction in which the two organisms live together in more or less intimate association.,,True
+RO.0002441,commensually interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which one benefits and the other is unaffected (GO).,,False
+RO.0002442,mutualistically interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which both organisms benefit from each other (GO).,,False
+RO.0002443,interacts with via parasite-host interaction,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which association is disadvantageous or destructive to one of the organisms (GO).,,False
+RO.0002444,parasite of,A parasite-host relationship where an organism benefits at the expense of another.,RO.0002445,False
+RO.0002445,parasitized by,Inverse of parasite of,,False
+RO.0002446,participates in a abiotic-biotic interaction with,"A biotic interaction relationship in which one partner is an organism and the other partner is inorganic. For example, the relationship between a sponge and the substrate to which is it anchored.",,False
+RO.0002447,phosphorylates,,,False
 RO.0002448,directly regulates activity of,"The entity A, immediately upstream of the entity B, has an activity that regulates an activity performed by B. For example, A and B may be gene products and binding of B by A regulates the kinase activity of B.
 
-A and B can be physically interacting but not necessarily. Immediately upstream means there are no intermediate entity between A and B."
+A and B can be physically interacting but not necessarily. Immediately upstream means there are no intermediate entity between A and B.",,False
 RO.0002449,directly negatively regulates activity of,"The entity A, immediately upstream of the entity B, has an activity that negatively regulates an activity performed by B. 
-For example, A and B may be gene products and binding of B by A negatively regulates the kinase activity of B."
+For example, A and B may be gene products and binding of B by A negatively regulates the kinase activity of B.",,False
 RO.0002450,directly positively regulates activity of,"The entity A, immediately upstream of the entity B, has an activity that positively regulates an activity performed by B. 
-For example, A and B may be gene products and binding of B by A positively regulates the kinase activity of B."
-RO.0002451,transmitted by,A relationship that holds between a disease and organism
-RO.0002452,has symptom,A relation that holds between a disease or an organism and a phenotype
-RO.0002453,host of,
-RO.0002454,has host,"X 'has host' y if and only if: x is an organism, y is an organism, and x can live on the surface of or within the body of y"
-RO.0002455,pollinates,
-RO.0002456,pollinated by,
-RO.0002457,acquires nutrients from,Inverse of provides nutrients for
-RO.0002458,preyed upon by,inverse of preys on
-RO.0002459,is vector for,
-RO.0002460,has vector,
-RO.0002461,partner in,
-RO.0002462,subject participant in,
-RO.0002463,target participant in,
-RO.0002464,helper property (not for use in curation),
-RO.0002465,is symbiosis,
-RO.0002466,is commensalism,
-RO.0002467,is mutualism,
-RO.0002468,is parasitism,
-RO.0002469,provides nutrients for,A biotic interaction where a material entity provides nutrition for an organism.
-RO.0002470,eats,A biotic interaction where one organism consumes a material entity through a type of mouth or other oral opening.
-RO.0002471,is eaten by,Inverse of eats
+For example, A and B may be gene products and binding of B by A positively regulates the kinase activity of B.",,False
+RO.0002451,transmitted by,A relationship that holds between a disease and organism,,False
+RO.0002452,has symptom,A relation that holds between a disease or an organism and a phenotype,,False
+RO.0002453,host of,,,False
+RO.0002454,has host,"X 'has host' y if and only if: x is an organism, y is an organism, and x can live on the surface of or within the body of y",,False
+RO.0002455,pollinates,,RO.0002456,False
+RO.0002456,pollinated by,,,False
+RO.0002457,acquires nutrients from,Inverse of provides nutrients for,RO.0002469,False
+RO.0002458,preyed upon by,inverse of preys on,,False
+RO.0002459,is vector for,,RO.0002460,False
+RO.0002460,has vector,,,False
+RO.0002461,partner in,,,False
+RO.0002462,subject participant in,,,False
+RO.0002463,target participant in,,,False
+RO.0002464,helper property (not for use in curation),,,False
+RO.0002465,is symbiosis,,,False
+RO.0002466,is commensalism,,,False
+RO.0002467,is mutualism,,,False
+RO.0002468,is parasitism,,,False
+RO.0002469,provides nutrients for,A biotic interaction where a material entity provides nutrition for an organism.,,False
+RO.0002470,eats,A biotic interaction where one organism consumes a material entity through a type of mouth or other oral opening.,,False
+RO.0002471,is eaten by,Inverse of eats,,False
 RO.0002472,is evidence for,"A relationship between a piece of evidence a and some entity b, where b is  an information content entity, material entity or process, and 
-the a supports either the existence of b, or the truth value of b."
-RO.0002473,composed primarily of,x composed_primarily_of y if and only if more than half of the mass of x is made from y or units of the same type as y.
-RO.0002476,child nucleus of,"c is a child nucleus of d if and only if c and d are both nuclei and parts of cells c' and d', where c' is derived from d' by mitosis and the genetic material in c is a copy of the generic material in d"
-RO.0002477,child nucleus of in hermaphrodite,A child nucleus relationship in which the cells are part of a hermaphroditic organism
-RO.0002478,child nucleus of in male,A child nucleus relationship in which the cells are part of a male organism
-RO.0002479,has part that occurs in,"p has part that occurs in c if and only if there exists some p1, such that p has_part p1, and p1 occurs in c."
-RO.0002480,ubiquitinates,An interaction relation between x and y in which x catalyzes a reaction in which one or more ubiquitin groups are added to y
-RO.0002481,is kinase activity,
-RO.0002482,is ubiquitination,
-RO.0002485,receives input from,
-RO.0002486,sends output to,
-RO.0002487,relation between physical entity and a process or stage,
-RO.0002488,existence starts during,x existence starts during y if and only if the time point at which x starts is after or equivalent to the time point at which y starts and before or equivalent to the time point at which y ends. Formally: x existence starts during y iff α(x) >= α(y) & α(x) <= ω(y).
-RO.0002489,existence starts with,x starts ends with y if and only if the time point at which x starts is equivalent to the time point at which y starts. Formally: x existence starts with y iff α(x) = α(y).
-RO.0002490,existence overlaps,x existence overlaps y if and only if either (a) the start of x is part of y or (b) the end of x is part of y. Formally: x existence starts and ends during y iff (α(x) >= α(y) & α(x) <= ω(y)) OR (ω(x) <= ω(y) & ω(x) >= α(y))
-RO.0002491,existence starts and ends during,x exists during y if and only if: 1) the time point at which x begins to exist is after or equal to the time point at which y begins and 2) the time point at which x ceases to exist is before or equal to the point at which y ends. Formally: x existence starts and ends during y iff α(x) >= α(y) & α(x) <= ω(y) & ω(x) <= ω(y) & ω(x) >= α(y)
-RO.0002492,existence ends during,x existence ends during y if and only if the time point at which x ends is before or equivalent to the time point at which y ends and after or equivalent to the point at which y starts. Formally: x existence ends during y iff ω(x) <= ω(y) and ω(x) >= α(y).
-RO.0002493,existence ends with,x existence ends with y if and only if the time point at which x ends is equivalent to the time point at which y ends. Formally: x existence ends with y iff ω(x) = ω(y).
-RO.0002494,transformation of,"x transformation of y if x is the immediate transformation of y, or is linked to y through a chain of transformation relationships"
-RO.0002495,immediate transformation of,"x immediate transformation of y iff x immediately succeeds y temporally at a time boundary t, and all of the matter present in x at t is present in y at t, and all the matter in y at t is present in x at t"
-RO.0002496,existence starts during or after,x existence starts during or after y if and only if the time point at which x starts is after or equivalent to the time point at which y starts. Formally: x existence starts during or after y iff α (x) >= α (y).
-RO.0002497,existence ends during or before,x existence ends during or before y if and only if the time point at which x ends is before or equivalent to the time point at which y ends.
-RO.0002500,causal agent in process,A relationship between a material entity and a process where the material entity has some causal role that influences the process
-RO.0002501,causal relation between processes,"p is causally related to q if and only if p or any part of p and q or any part of q are linked by a chain of events where each event pair is one where the execution of p influences the execution of q. p may be upstream, downstream, part of, or a container of q."
-RO.0002502,depends on,
-RO.0002503,towards,"q towards e2 if and only if q is a relational quality such that q inheres-in some e, and e != e2 and q is dependent on e2"
-RO.0002505,has intermediate,"p has intermediate c if and only if p has parts p1, p2 and p1 has output c, and p2 has input c"
-RO.0002506,causal relation between entities,
-RO.0002507,determined by,"s determined by f if and only if s is a type of system, and f is a material entity that is part of s, such that f exerts a strong causal influence on the functioning of s, and the removal of f would cause the collapse of s."
-RO.0002508,determines,inverse of determined by
-RO.0002509,determined by part of,"s 'determined by part of' w if and only if there exists some f such that (1) s 'determined by' f and (2) f part_of w, or f=w."
-RO.0002510,transcribed from,x is transcribed from y if and only if x is synthesized from template y
-RO.0002511,transcribed to,inverse of transcribed from
-RO.0002512,ribosomal translation of,x is the ribosomal translation of y if and only if a ribosome reads x through a series of triplet codon-amino acid adaptor activities (GO:0030533) and produces y
-RO.0002513,ribosomally translates to,inverse of ribosomal translation of
-RO.0002514,sequentially related to,A relation that holds between two entities that have the property of being sequences or having sequences. 
-RO.0002515,sequentially adjacent to,x is sequentially adjacent to y iff x and y do not overlap and if there are no base units intervening between x and y
-RO.0002516,has start sequence,"x has start sequence y if the start of x is identical to the start of y, and x has y as a subsequence"
-RO.0002517,is start sequence of,inverse of has start sequence
-RO.0002518,has end sequence,"x has end sequence y if the end of x is identical to the end of y, and x has y as a subsequence"
-RO.0002519,is end sequence of,inverse of has end sequence
-RO.0002520,is consecutive sequence of,"x is a consecutive sequence of y iff x has subsequence y, and all the parts of x are made of zero or more repetitions of y or sequences as the same type as y."
-RO.0002521,is sequentially aligned with,x is sequentially aligned with if a significant portion bases of x and y correspond in terms of their base type and their relative ordering
-RO.0002522,bounds sequence of,"x bounds the sequence of y iff the upstream-most part of x is upstream of or coincident with the upstream-most part of y, and the downstream-most part of x is downstream of or coincident with the downstream-most part of y"
-RO.0002523,is bound by sequence of,inverse of bounds sequence of
-RO.0002524,has subsequence,x has subsequence y iff all of the sequence parts of y are sequence parts of x
-RO.0002525,is subsequence of,inverse of has subsequence
-RO.0002526,overlaps sequence of,x overlaps the sequence of y if and only if x has a subsequence z and z is a subsequence of y.
-RO.0002527,does not overlap sequence of,x does not overlap the sequence of y if and only if there is no z such that x has a subsequence z and z is a subsequence of y.
-RO.0002528,is upstream of sequence of,inverse of downstream of sequence of
-RO.0002529,is downstream of sequence of,"x is downstream of the sequence of y iff either (1) x and y have sequence units, and all units of x are downstream of all units of y, or (2) x and y are sequence units, and x is either immediately downstream of y, or transitively downstream of y."
-RO.0002530,is immediately downstream of sequence of,"x is immediately downstream of the sequence of y iff either (1) x and y have sequence units, and all units of x are downstream of all units of y, and x is sequentially adjacent to y, or (2) x and y are sequence units, in which case the immediately downstream relation is primitive and defined by context: for DNA bases, y would be adjacent and 5' to y"
-RO.0002531,is immediately upstream of sequence of,inverse of immediately downstream of
-RO.0002551,has skeleton,A relation between a segment or subdivision of an organism and the maximal subdivision of material entities that provides structural support for that segment or subdivision.
-RO.0002552,results in ending of,"p results in the end of s if p results in a change of state in s whereby s either ceases to exist, or s becomes functionally impaired or s has its fate committed such that it is put on a path to be degraded."
-RO.0002553,hyperparasite of,x is a hyperparasite of y iff x is a parasite of a parasite of the target organism y
-RO.0002554,hyperparasitized by,inverse of hyperparasite of
-RO.0002555,allelopath of,
-RO.0002556,pathogen of,Inverse of has pathogen
-RO.0002557,has pathogen,A host interaction where the smaller of the two members of a symbiosis causes a disease in the larger member
-RO.0002558,has evidence,inverse of is evidence for
-RO.0002559,causally influenced by,
-RO.0002563,interaction relation helper property,
-RO.0002564,molecular interaction relation helper property,
-RO.0002565,results in movement of,Holds between p and c when p is locomotion process and the outcome of this process is the change of location of c
-RO.0002566,causally influences,"The entity or characteristic A is causally upstream of the entity or characteristic B, A having an effect on B. An entity corresponds to any biological type of entity as long as a mass is measurable. A characteristic corresponds to a particular specificity of an entity (e.g., phenotype, shape, size)."
-RO.0002567,biomechanically related to,A relation that holds between elements of a musculoskeletal system or its analogs.
-RO.0002568,has muscle antagonist,"m1 has_muscle_antagonist m2 iff m1 has_muscle_insertion s, m2 has_muscle_insection s, m1 acts in opposition to m2, and m2 is responsible for returning the structure to its initial position."
-RO.0002569,has branching part,inverse of branching part of
-RO.0002570,conduit for,"x is a conduit for y iff y overlaps through the lumen_of of x, and y has parts on either side of the lumen of x."
-RO.0002571,lumen of,x lumen_of y iff x is the space or substance that is part of y and does not cross any of the inner membranes or boundaries of y that is maximal with respect to the volume of the convex hull.
-RO.0002572,luminal space of,s is luminal space of x iff s is lumen_of x and s is an immaterial entity
-RO.0002573,has modifier,A relation that holds between an attribute or a qualifier and another attribute.
-RO.0002574,participates in a biotic-biotic interaction with,
-RO.0002576,skeleton of,inverse of has skeleton
-RO.0002578,directly regulates,p directly regulates q iff p is immediately causally upstream of q and p regulates q.
-RO.0002583,existence starts at end of,holds between x and y if and only if the time point at which x starts is equivalent to the time point at which y ends. Formally: iff α(x) = ω(y).
-RO.0002584,has part structure that is capable of,s 'has part structure that is capable of' p if and only if there exists some part x such that s 'has part' x and x 'capable of' p
-RO.0002585,results in closure of,
-RO.0002586,results in breakdown of,p results in breakdown of c if and only if the execution of p leads to c no longer being present at the end of p
-RO.0002587,results in synthesis of,
-RO.0002588,results in assembly of,
-RO.0002589,results in catabolism of,"p results in catabolism of c if and only if p is a catabolic process, and the execution of p results in c being broken into smaller parts with energy being released."
-RO.0002590,results in disassembly of,
-RO.0002591,results in remodeling of,
-RO.0002592,results in organization of,"p results in organization of c iff p results in the assembly, arrangement of constituent parts, or disassembly of c"
-RO.0002593,existence ends at start of,holds between x and y if and only if the time point at which x ends is equivalent to the time point at which y starts. Formally: iff ω(x) = α(y).
-RO.0002595,causal relation between material entity and a process,"A relationship that holds between a material entity and a process in which causality is involved, with either the material entity or some part of the material entity exerting some influence over the process, or the process influencing some aspect of the material entity."
-RO.0002596,capable of regulating,"Holds between c and p if and only if c is capable of some activity a, and a regulates p."
-RO.0002597,capable of negatively regulating,"Holds between c and p if and only if c is capable of some activity a, and a negatively regulates p."
-RO.0002598,capable of positively regulating,"Holds between c and p if and only if c is capable of some activity a, and a positively regulates p."
-RO.0002599,capable of inhibiting or preventing pathological process,"Holds between a material entity c and a pathological process p if and only if c is capable of some activity a, where a inhibits p."
-RO.0002600,capable of upregulating or causing pathological process,"Holds between a material entity c and a pathological process p if and only if c is capable of some activity a, where a negatively regulates p."
-RO.0002606,is substance that treats,"c is a substance that treats d if c is a material entity (such as a small molecule or compound) and d is a pathological process, phenotype or disease, and c is capable of some activity that negative regulates or decreases the magnitude of d."
-RO.0002607,is marker for,"c is marker for d iff the presence or occurrence of d is correlated with the presence of occurrence of c, and the observation of c is used to infer the presence or occurrence of d. Note that this does not imply that c and d are in a direct causal relationship, as it may be the case that there is a third entity e that stands in a direct causal relationship with c and d."
-RO.0002608,process has causal agent,Inverse of 'causal agent in process'
-RO.0002609,obsolete related via dependence to,"A relationship that holds between two entities, where the relationship holds based on the presence or absence of statistical dependence relationship. The entities may be statistical variables, or they may be other kinds of entities such as diseases, chemical entities or processes."
-RO.0002610,correlated with,"A relationship that holds between two entities, where the entities exhibit a statistical dependence relationship. The entities may be statistical variables, or they may be other kinds of entities such as diseases, chemical entities or processes."
-RO.0002614,is evidence with support from,A relationship between a piece of evidence and an entity that plays a role in supporting that evidence.
-RO.0002615,has model,Inverse of is-model-of
-RO.0002616,related via evidence or inference to,
-RO.0002618,visits,
-RO.0002619,visited by,
-RO.0002622,visits flowers of,
-RO.0002623,has flowers visited by,
-RO.0002624,lays eggs in,
-RO.0002625,has eggs laid in by,
-RO.0002626,kills,
-RO.0002627,is killed by,
-RO.0002629,directly positively regulates,"p directly positively regulates q iff p is immediately causally upstream of q, and p positively regulates q."
-RO.0002630,directly negatively regulates,"p directly negatively regulates q iff p is immediately causally upstream of q, and p negatively regulates q."
-RO.0002632,ectoparasite of,A sub-relation of parasite-of in which the parasite lives on or in the integumental system of the host
-RO.0002633,has ectoparasite,inverse of ectoparasite of
-RO.0002634,endoparasite of,
-RO.0002635,has endoparasite,
-RO.0002636,mesoparasite of,A sub-relation of parasite-of in which the parasite is partially an endoparasite and partially an ectoparasite
-RO.0002637,has mesoparasite,inverse of mesoparasite of
-RO.0002638,intercellular endoparasite of,A sub-relation of endoparasite-of in which the parasite inhabits the spaces between host cells.
-RO.0002639,has intercellular endoparasite,inverse of intercellular endoparasite of
-RO.0002640,intracellular endoparasite of,A sub-relation of endoparasite-of in which the parasite inhabits host cells.
-RO.0002641,has intracellular endoparasite,inverse of intracellular endoparasite of
-RO.0002801,co-roosts with,"Two or more individuals sharing the same roost site (cave, mine, tree or tree hollow, animal burrow, leaf tent, rock crack, space in man-made structure, etc.). Individuals that are sharing a communal roost may be said to be co-roosting. The roost may be either a day roost where the individuals rest during daytime hours, or a night roost where individuals roost to feed, groom, or rest in between flights and/or foraging bouts.  Communal roosting as thus defined is an umbrella term within which different specialized types -- which are not mutually exclusive -- may be recognized based on taxonomy and the temporal and spatial relationships of the individuals that are co-roosting."
-RO.0002802,reservoir host of,A relation between a host organism and a hosted organism in which the hosted organism naturally occurs in an indefinitely maintained reservoir provided by the host.
-RO.0002803,has reservoir host,inverse of reservoir host of
-RO.0003000,produces,"a produces b if some process that occurs_in a has_output b, where a and b are material entities. Examples: hybridoma cell line produces monoclonal antibody reagent; chondroblast produces avascular GAG-rich matrix."
-RO.0003001,produced by,a produced_by b iff some process that occurs_in b has_output a.
-RO.0003002,represses expression of,Holds between entity A (a transcription factor) and a nucleic acid B if and only if A down-regulates the expression of B. The nucleic acid can be a gene or an mRNA.
-RO.0003003,increases expression of,Holds between entity A (a transcription factor) and nucleic acid B if and only if A up-regulates the expression of B. The nucleic acid can be a gene or mRNA.
-RO.0003301,has role in modeling,"A relation between a biological, experimental, or computational artifact and an entity it is used to study, in virtue of its replicating or approximating features of the studied entity."
-RO.0003302,causes or contributes to condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some causal or contributing role that influences the condition."
-RO.0003303,causes condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some causal role for the condition."
-RO.0003304,contributes to condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some contributing role that influences the condition."
-RO.0003305,contributes to severity of condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity influences the severity with which a condition manifests in an individual."
-RO.0003306,contributes to frequency of condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity influences the frequency of the condition in a population."
-RO.0003307,ameliorates condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the presence of the entity reduces or eliminates some or all aspects of the condition."
-RO.0003308,correlated with condition,A relationship between an entity and a condition (phenotype or disease) with which it exhibits a statistical dependence relationship.
-RO.0003309,exacerbates condition,"A relationship between an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) and a condition (a phenotype or disease), where the presence of the entity worsens some or all aspects of the condition."
-RO.0003310,condition ameliorated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are reduced or eliminated by the presence of the entity."
-RO.0003311,condition exacerbated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are worsened by the presence of the entity."
-RO.0004000,condition has genetic basis in,
-RO.0004001,has material basis in gain of function germline mutation in,
-RO.0004002,has material basis in loss of function germline mutation in,
-RO.0004003,has material basis in germline mutation in,
-RO.0004004,has material basis in somatic mutation in,
-RO.0004005,has major susceptibility factor,
-RO.0004006,has partial material basis in germline mutation in,
-RO.0004007,has primary input or output,p 'has primary input ot output' c iff either (a) p 'has primary input' c or (b) p 'has primary output' c.
-RO.0004008,has primary output,"p has primary output c if (a) p has output c and (b) the goal of process is to modify, produce, or transform c."
-RO.0004009,has primary input,"p has primary input c if (a) p has input c and (b) the goal of process is to modify, consume, or transform c."
-RO.0004010,is genetic basis for condition,
-RO.0004011,is causal gain of function germline mutation of in,"Relates a gene to condition, such that a mutation in this gene in a germ cell provides a new function of the corresponding product and that is sufficient to produce the condition and that can be passed on to offspring[modified from orphanet]."
-RO.0004012,is causal loss of function germline mutation of in,"Relates a gene to condition, such that a mutation in this gene in a germ cell impairs the function of the corresponding product and that is sufficient to produce the condition and that can be passed on to offspring[modified from orphanet]."
-RO.0004013,is causal germline mutation in,"Relates a gene to condition, such that a mutation in this gene is sufficient to produce the condition and that can be passed on to offspring[modified from orphanet]."
-RO.0004014,is causal somatic mutation in,"Relates a gene to condition, such that a mutation in this gene is sufficient to produce the condition but that cannot be passed on to offspring[modified from orphanet]."
-RO.0004015,is causal susceptibility factor for,"Relates a gene to condition, such that a mutation in this gene predisposes to the development of a condition  and that is necessary but not sufficient to develop the condition[modified from orphanet]."
-RO.0004016,is causal germline mutation partially giving rise to,"Relates a gene to condition, such that a mutation in this gene partially contributes to the presentation of this condition[modified from orphanet]."
-RO.0004017,realizable has basis in,
-RO.0004018,is basis for realizable,
-RO.0004019,disease has basis in,
-RO.0004020,disease has basis in dysfunction of,A relation that holds between the disease and a material entity where the physical basis of the disease is a disorder of that material entity that affects its function.
-RO.0004021,disease has basis in disruption of,A relation that holds between the disease and a process where the physical basis of the disease disrupts execution of a key biological process.
-RO.0004022,disease has basis in feature,A relation that holds between the disease and a feature (a phenotype or other disease) where the physical basis of the disease is the feature.
-RO.0004023,causal relationship with disease as subject,
-RO.0004024,disease causes disruption of,A relationship between a disease and a process where the disease process disrupts the execution of the process.
-RO.0004025,disease causes dysfunction of,
-RO.0004026,disease has location,A relationship between a disease and an anatomical entity where the disease has one or more features that are located in that entity.
-RO.0004027,disease has inflammation site,A relationship between a disease and an anatomical entity where the disease is triggered by an inflammatory response to stimuli occurring in the anatomical entity
-RO.0004028,realized in response to stimulus,A relationship between a realizable entity R (e.g. function or disposition) and a material entity M where R is realized in response to a process that has an input stimulus of M.
-RO.0004029,disease has feature,"A relationship between a disease and some feature of that disease, where the feature is either a phenotype or an isolated disease."
-RO.0004030,disease arises from alteration in structure,"A relationship between a disease and an anatomical structure where the material basis of the disease is some pathological change in the structure. Anatomical structure includes cellular and sub-cellular entities, such as chromosome and organelles."
-RO.0004031,enables subfunction,"Holds between an entity and an process P where the entity enables some larger compound process, and that larger process has-part P."
-RO.0004032,"acts upstream of or within, positive effect",
-RO.0004033,"acts upstream of or within, negative effect",
-RO.0004034,"acts upstream of, positive effect","c 'acts upstream of, positive effect' p if c is enables f, and f is causally upstream of p, and the direction of f is positive"
-RO.0004035,"acts upstream of, negative effect","c 'acts upstream of, negative effect' p if c is enables f, and f is causally upstream of p, and the direction of f is negative"
-RO.0004046,"causally upstream of or within, negative effect",
-RO.0004047,"causally upstream of or within, positive effect",
-RO.0004096,DEPRECATED inheres in,
-RO.0004097,DEPRECATED bearer of,
-RO.0007000,has driver,"A relation between two entities, in which one of the entities is any natural or human-influenced factor that directly or indirectly causes a change in the other entity."
-RO.0007001,has disease driver,"A relation between an entity and a disease of a host, in which the entity is not part of the host itself, and the condition results in pathological processes."
-RO.0008501,epiphyte of,An interaction relationship wherein a plant or algae is living on the outside surface of another plant.
-RO.0008502,has epiphyte,inverse of epiphyte of
-RO.0008503,kleptoparasite of,"A sub-relation of parasite of in which a parasite steals resources from another organism, usually food or nest material"
-RO.0008504,kleptoparasitized by,inverse of kleptoparasite of
-RO.0008505,creates habitat for,An interaction relationship wherein one organism creates a structure or environment that is lived in by another organism.
-RO.0008506,ecologically co-occurs with,An interaction relationship describing organisms that often occur together at the same time and space or in the same environment.
-RO.0008507,lays eggs on,An interaction relationship in which organism a lays eggs on the outside surface of organism b. Organism b is neither helped nor harmed in the process of egg laying or incubation.
-RO.0008508,has eggs laid on by,inverse of lays eggs on
-RO.0008509,has roost,"x 'has roost' y if and only if: x is an organism, y is a habitat, and y can support rest behaviors x."
-RO.0009001,has substance added,"""has substance added"" is a relation existing between a (physical) entity and a substance in which the entity has had the substance added to it at some point in time."
-RO.0009002,has substance removed,"""has substance removed"" is a relation existing between two physical entities in which the first entity has had the second entity (a substance) removed from it at some point in time."
-RO.0009003,immersed in,"""immersed in"" is a relation between a (physical) entity and a fluid substance in which the entity is wholely or substantially surrounded by the substance."
-RO.0009004,has consumer,'has consumer' is a relation between a material entity and an organism in which the former can normally be digested or otherwise absorbed by the latter without immediate or persistent ill effect.
-RO.0009005,has primary substance added,'has primary substance added' indicates that an entity has had the given substance added to it in a proportion greater than any other added substance.
-RO.0009006,assay measures characteristic,"A relation between an assay and a characteristic, in which the assay generates a data item which is a measure of a characteristic."
-RO.0009007,characteristic measured by assay,Inverse of 'assay measures characteristic'
-RO.0009501,realized in response to,"r 'realized in response to' s iff, r is a realizable (e.g. a plant trait such as responsivity to drought), s is an environmental stimulus (a process), and s directly causes the realization of r."
-RO.0010001,generically depends on,A generically dependent continuant *b* generically depends on an independent continuant *c* at time *t* means: there inheres in *c* a specifically deendent continuant which concretizes *b* at *t*.
-RO.0010002,is carrier of,*b* is carrier of *c* at time *t* if and only if *c* *g-depends on* *b* at *t*
-RO.0011002,regulates activity of,"The entity A has an activity that regulates an activity of the entity B. For example, A and B are gene products where the catalytic activity of A regulates the kinase activity of B."
-RO.0011003,regulates quantity of,The entity A has an activity that regulates the quantity or abundance or concentration of the entity B.
-RO.0011004,indirectly regulates activity of,The entity A is not immediately upstream of the entity B but A has an activity that regulates an activity performed by B.
+the a supports either the existence of b, or the truth value of b.",RO.0002558,False
+RO.0002473,composed primarily of,x composed_primarily_of y if and only if more than half of the mass of x is made from y or units of the same type as y.,,False
+RO.0002476,child nucleus of,"c is a child nucleus of d if and only if c and d are both nuclei and parts of cells c' and d', where c' is derived from d' by mitosis and the genetic material in c is a copy of the generic material in d",,False
+RO.0002477,child nucleus of in hermaphrodite,A child nucleus relationship in which the cells are part of a hermaphroditic organism,,False
+RO.0002478,child nucleus of in male,A child nucleus relationship in which the cells are part of a male organism,,False
+RO.0002479,has part that occurs in,"p has part that occurs in c if and only if there exists some p1, such that p has_part p1, and p1 occurs in c.",,False
+RO.0002480,ubiquitinates,An interaction relation between x and y in which x catalyzes a reaction in which one or more ubiquitin groups are added to y,,False
+RO.0002481,is kinase activity,,,False
+RO.0002482,is ubiquitination,,,False
+RO.0002485,receives input from,,RO.0002486,False
+RO.0002486,sends output to,,,False
+RO.0002487,relation between physical entity and a process or stage,,,False
+RO.0002488,existence starts during,x existence starts during y if and only if the time point at which x starts is after or equivalent to the time point at which y starts and before or equivalent to the time point at which y ends. Formally: x existence starts during y iff α(x) >= α(y) & α(x) <= ω(y).,,False
+RO.0002489,existence starts with,x starts ends with y if and only if the time point at which x starts is equivalent to the time point at which y starts. Formally: x existence starts with y iff α(x) = α(y).,,False
+RO.0002490,existence overlaps,x existence overlaps y if and only if either (a) the start of x is part of y or (b) the end of x is part of y. Formally: x existence starts and ends during y iff (α(x) >= α(y) & α(x) <= ω(y)) OR (ω(x) <= ω(y) & ω(x) >= α(y)),,False
+RO.0002491,existence starts and ends during,x exists during y if and only if: 1) the time point at which x begins to exist is after or equal to the time point at which y begins and 2) the time point at which x ceases to exist is before or equal to the point at which y ends. Formally: x existence starts and ends during y iff α(x) >= α(y) & α(x) <= ω(y) & ω(x) <= ω(y) & ω(x) >= α(y),,False
+RO.0002492,existence ends during,x existence ends during y if and only if the time point at which x ends is before or equivalent to the time point at which y ends and after or equivalent to the point at which y starts. Formally: x existence ends during y iff ω(x) <= ω(y) and ω(x) >= α(y).,,False
+RO.0002493,existence ends with,x existence ends with y if and only if the time point at which x ends is equivalent to the time point at which y ends. Formally: x existence ends with y iff ω(x) = ω(y).,,False
+RO.0002494,transformation of,"x transformation of y if x is the immediate transformation of y, or is linked to y through a chain of transformation relationships",,False
+RO.0002495,immediate transformation of,"x immediate transformation of y iff x immediately succeeds y temporally at a time boundary t, and all of the matter present in x at t is present in y at t, and all the matter in y at t is present in x at t",,False
+RO.0002496,existence starts during or after,x existence starts during or after y if and only if the time point at which x starts is after or equivalent to the time point at which y starts. Formally: x existence starts during or after y iff α (x) >= α (y).,,False
+RO.0002497,existence ends during or before,x existence ends during or before y if and only if the time point at which x ends is before or equivalent to the time point at which y ends.,,False
+RO.0002500,causal agent in process,A relationship between a material entity and a process where the material entity has some causal role that influences the process,RO.0002608,False
+RO.0002501,causal relation between processes,"p is causally related to q if and only if p or any part of p and q or any part of q are linked by a chain of events where each event pair is one where the execution of p influences the execution of q. p may be upstream, downstream, part of, or a container of q.",,False
+RO.0002502,depends on,,,False
+RO.0002503,towards,"q towards e2 if and only if q is a relational quality such that q inheres-in some e, and e != e2 and q is dependent on e2",,False
+RO.0002505,has intermediate,"p has intermediate c if and only if p has parts p1, p2 and p1 has output c, and p2 has input c",,False
+RO.0002506,causal relation between entities,,,False
+RO.0002507,determined by,"s determined by f if and only if s is a type of system, and f is a material entity that is part of s, such that f exerts a strong causal influence on the functioning of s, and the removal of f would cause the collapse of s.",RO.0002508,False
+RO.0002508,determines,inverse of determined by,,False
+RO.0002509,determined by part of,"s 'determined by part of' w if and only if there exists some f such that (1) s 'determined by' f and (2) f part_of w, or f=w.",,False
+RO.0002510,transcribed from,x is transcribed from y if and only if x is synthesized from template y,RO.0002511,False
+RO.0002511,transcribed to,inverse of transcribed from,,False
+RO.0002512,ribosomal translation of,x is the ribosomal translation of y if and only if a ribosome reads x through a series of triplet codon-amino acid adaptor activities (GO:0030533) and produces y,RO.0002513,False
+RO.0002513,ribosomally translates to,inverse of ribosomal translation of,,False
+RO.0002514,sequentially related to,A relation that holds between two entities that have the property of being sequences or having sequences. ,,False
+RO.0002515,sequentially adjacent to,x is sequentially adjacent to y iff x and y do not overlap and if there are no base units intervening between x and y,,False
+RO.0002516,has start sequence,"x has start sequence y if the start of x is identical to the start of y, and x has y as a subsequence",RO.0002517,False
+RO.0002517,is start sequence of,inverse of has start sequence,,False
+RO.0002518,has end sequence,"x has end sequence y if the end of x is identical to the end of y, and x has y as a subsequence",RO.0002519,False
+RO.0002519,is end sequence of,inverse of has end sequence,,False
+RO.0002520,is consecutive sequence of,"x is a consecutive sequence of y iff x has subsequence y, and all the parts of x are made of zero or more repetitions of y or sequences as the same type as y.",,False
+RO.0002521,is sequentially aligned with,x is sequentially aligned with if a significant portion bases of x and y correspond in terms of their base type and their relative ordering,,True
+RO.0002522,bounds sequence of,"x bounds the sequence of y iff the upstream-most part of x is upstream of or coincident with the upstream-most part of y, and the downstream-most part of x is downstream of or coincident with the downstream-most part of y",RO.0002523,False
+RO.0002523,is bound by sequence of,inverse of bounds sequence of,,False
+RO.0002524,has subsequence,x has subsequence y iff all of the sequence parts of y are sequence parts of x,RO.0002525,False
+RO.0002525,is subsequence of,inverse of has subsequence,,False
+RO.0002526,overlaps sequence of,x overlaps the sequence of y if and only if x has a subsequence z and z is a subsequence of y.,,True
+RO.0002527,does not overlap sequence of,x does not overlap the sequence of y if and only if there is no z such that x has a subsequence z and z is a subsequence of y.,,True
+RO.0002528,is upstream of sequence of,inverse of downstream of sequence of,,False
+RO.0002529,is downstream of sequence of,"x is downstream of the sequence of y iff either (1) x and y have sequence units, and all units of x are downstream of all units of y, or (2) x and y are sequence units, and x is either immediately downstream of y, or transitively downstream of y.",RO.0002529,False
+RO.0002530,is immediately downstream of sequence of,"x is immediately downstream of the sequence of y iff either (1) x and y have sequence units, and all units of x are downstream of all units of y, and x is sequentially adjacent to y, or (2) x and y are sequence units, in which case the immediately downstream relation is primitive and defined by context: for DNA bases, y would be adjacent and 5' to y",,False
+RO.0002531,is immediately upstream of sequence of,inverse of immediately downstream of,,False
+RO.0002551,has skeleton,A relation between a segment or subdivision of an organism and the maximal subdivision of material entities that provides structural support for that segment or subdivision.,RO.0002576,False
+RO.0002552,results in ending of,"p results in the end of s if p results in a change of state in s whereby s either ceases to exist, or s becomes functionally impaired or s has its fate committed such that it is put on a path to be degraded.",,False
+RO.0002553,hyperparasite of,x is a hyperparasite of y iff x is a parasite of a parasite of the target organism y,RO.0002554,False
+RO.0002554,hyperparasitized by,inverse of hyperparasite of,,False
+RO.0002555,allelopath of,,,False
+RO.0002556,pathogen of,Inverse of has pathogen,RO.0002557,False
+RO.0002557,has pathogen,A host interaction where the smaller of the two members of a symbiosis causes a disease in the larger member,,False
+RO.0002558,has evidence,inverse of is evidence for,,False
+RO.0002559,causally influenced by,,RO.0002566,False
+RO.0002563,interaction relation helper property,,,False
+RO.0002564,molecular interaction relation helper property,,,False
+RO.0002565,results in movement of,Holds between p and c when p is locomotion process and the outcome of this process is the change of location of c,,False
+RO.0002566,causally influences,"The entity or characteristic A is causally upstream of the entity or characteristic B, A having an effect on B. An entity corresponds to any biological type of entity as long as a mass is measurable. A characteristic corresponds to a particular specificity of an entity (e.g., phenotype, shape, size).",,False
+RO.0002567,biomechanically related to,A relation that holds between elements of a musculoskeletal system or its analogs.,,False
+RO.0002568,has muscle antagonist,"m1 has_muscle_antagonist m2 iff m1 has_muscle_insertion s, m2 has_muscle_insection s, m1 acts in opposition to m2, and m2 is responsible for returning the structure to its initial position.",,False
+RO.0002569,has branching part,inverse of branching part of,,False
+RO.0002570,conduit for,"x is a conduit for y iff y overlaps through the lumen_of of x, and y has parts on either side of the lumen of x.",,False
+RO.0002571,lumen of,x lumen_of y iff x is the space or substance that is part of y and does not cross any of the inner membranes or boundaries of y that is maximal with respect to the volume of the convex hull.,,False
+RO.0002572,luminal space of,s is luminal space of x iff s is lumen_of x and s is an immaterial entity,,False
+RO.0002573,has modifier,A relation that holds between an attribute or a qualifier and another attribute.,,False
+RO.0002574,participates in a biotic-biotic interaction with,,,False
+RO.0002576,skeleton of,inverse of has skeleton,,False
+RO.0002578,directly regulates,p directly regulates q iff p is immediately causally upstream of q and p regulates q.,,False
+RO.0002583,existence starts at end of,holds between x and y if and only if the time point at which x starts is equivalent to the time point at which y ends. Formally: iff α(x) = ω(y).,,False
+RO.0002584,has part structure that is capable of,s 'has part structure that is capable of' p if and only if there exists some part x such that s 'has part' x and x 'capable of' p,,False
+RO.0002585,results in closure of,,,False
+RO.0002586,results in breakdown of,p results in breakdown of c if and only if the execution of p leads to c no longer being present at the end of p,,False
+RO.0002587,results in synthesis of,,,False
+RO.0002588,results in assembly of,,,False
+RO.0002589,results in catabolism of,"p results in catabolism of c if and only if p is a catabolic process, and the execution of p results in c being broken into smaller parts with energy being released.",,False
+RO.0002590,results in disassembly of,,,False
+RO.0002591,results in remodeling of,,,False
+RO.0002592,results in organization of,"p results in organization of c iff p results in the assembly, arrangement of constituent parts, or disassembly of c",,False
+RO.0002593,existence ends at start of,holds between x and y if and only if the time point at which x ends is equivalent to the time point at which y starts. Formally: iff ω(x) = α(y).,,False
+RO.0002595,causal relation between material entity and a process,"A relationship that holds between a material entity and a process in which causality is involved, with either the material entity or some part of the material entity exerting some influence over the process, or the process influencing some aspect of the material entity.",,False
+RO.0002596,capable of regulating,"Holds between c and p if and only if c is capable of some activity a, and a regulates p.",,False
+RO.0002597,capable of negatively regulating,"Holds between c and p if and only if c is capable of some activity a, and a negatively regulates p.",,False
+RO.0002598,capable of positively regulating,"Holds between c and p if and only if c is capable of some activity a, and a positively regulates p.",,False
+RO.0002599,capable of inhibiting or preventing pathological process,"Holds between a material entity c and a pathological process p if and only if c is capable of some activity a, where a inhibits p.",,False
+RO.0002600,capable of upregulating or causing pathological process,"Holds between a material entity c and a pathological process p if and only if c is capable of some activity a, where a negatively regulates p.",,False
+RO.0002606,is substance that treats,"c is a substance that treats d if c is a material entity (such as a small molecule or compound) and d is a pathological process, phenotype or disease, and c is capable of some activity that negative regulates or decreases the magnitude of d.",,False
+RO.0002607,is marker for,"c is marker for d iff the presence or occurrence of d is correlated with the presence of occurrence of c, and the observation of c is used to infer the presence or occurrence of d. Note that this does not imply that c and d are in a direct causal relationship, as it may be the case that there is a third entity e that stands in a direct causal relationship with c and d.",,False
+RO.0002608,process has causal agent,Inverse of 'causal agent in process',,False
+RO.0002609,obsolete related via dependence to,"A relationship that holds between two entities, where the relationship holds based on the presence or absence of statistical dependence relationship. The entities may be statistical variables, or they may be other kinds of entities such as diseases, chemical entities or processes.",,False
+RO.0002610,correlated with,"A relationship that holds between two entities, where the entities exhibit a statistical dependence relationship. The entities may be statistical variables, or they may be other kinds of entities such as diseases, chemical entities or processes.",,False
+RO.0002614,is evidence with support from,A relationship between a piece of evidence and an entity that plays a role in supporting that evidence.,,False
+RO.0002615,has model,Inverse of is-model-of,RO.0003301,False
+RO.0002616,related via evidence or inference to,,,False
+RO.0002618,visits,,RO.0002619,False
+RO.0002619,visited by,,,False
+RO.0002622,visits flowers of,,RO.0002623,False
+RO.0002623,has flowers visited by,,,False
+RO.0002624,lays eggs in,,RO.0002625,False
+RO.0002625,has eggs laid in by,,,False
+RO.0002626,kills,,RO.0002627,False
+RO.0002627,is killed by,,,False
+RO.0002629,directly positively regulates,"p directly positively regulates q iff p is immediately causally upstream of q, and p positively regulates q.",,False
+RO.0002630,directly negatively regulates,"p directly negatively regulates q iff p is immediately causally upstream of q, and p negatively regulates q.",,False
+RO.0002632,ectoparasite of,A sub-relation of parasite-of in which the parasite lives on or in the integumental system of the host,RO.0002633,False
+RO.0002633,has ectoparasite,inverse of ectoparasite of,,False
+RO.0002634,endoparasite of,,RO.0002635,False
+RO.0002635,has endoparasite,,,False
+RO.0002636,mesoparasite of,A sub-relation of parasite-of in which the parasite is partially an endoparasite and partially an ectoparasite,RO.0002637,False
+RO.0002637,has mesoparasite,inverse of mesoparasite of,,False
+RO.0002638,intercellular endoparasite of,A sub-relation of endoparasite-of in which the parasite inhabits the spaces between host cells.,RO.0002639,False
+RO.0002639,has intercellular endoparasite,inverse of intercellular endoparasite of,,False
+RO.0002640,intracellular endoparasite of,A sub-relation of endoparasite-of in which the parasite inhabits host cells.,RO.0002641,False
+RO.0002641,has intracellular endoparasite,inverse of intracellular endoparasite of,,False
+RO.0002801,co-roosts with,"Two or more individuals sharing the same roost site (cave, mine, tree or tree hollow, animal burrow, leaf tent, rock crack, space in man-made structure, etc.). Individuals that are sharing a communal roost may be said to be co-roosting. The roost may be either a day roost where the individuals rest during daytime hours, or a night roost where individuals roost to feed, groom, or rest in between flights and/or foraging bouts.  Communal roosting as thus defined is an umbrella term within which different specialized types -- which are not mutually exclusive -- may be recognized based on taxonomy and the temporal and spatial relationships of the individuals that are co-roosting.",,True
+RO.0002802,reservoir host of,A relation between a host organism and a hosted organism in which the hosted organism naturally occurs in an indefinitely maintained reservoir provided by the host.,RO.0002803,False
+RO.0002803,has reservoir host,inverse of reservoir host of,,False
+RO.0003000,produces,"a produces b if some process that occurs_in a has_output b, where a and b are material entities. Examples: hybridoma cell line produces monoclonal antibody reagent; chondroblast produces avascular GAG-rich matrix.",RO.0003001,False
+RO.0003001,produced by,a produced_by b iff some process that occurs_in b has_output a.,,False
+RO.0003002,represses expression of,Holds between entity A (a transcription factor) and a nucleic acid B if and only if A down-regulates the expression of B. The nucleic acid can be a gene or an mRNA.,,False
+RO.0003003,increases expression of,Holds between entity A (a transcription factor) and nucleic acid B if and only if A up-regulates the expression of B. The nucleic acid can be a gene or mRNA.,,False
+RO.0003301,has role in modeling,"A relation between a biological, experimental, or computational artifact and an entity it is used to study, in virtue of its replicating or approximating features of the studied entity.",,False
+RO.0003302,causes or contributes to condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some causal or contributing role that influences the condition.",,False
+RO.0003303,causes condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some causal role for the condition.",,False
+RO.0003304,contributes to condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some contributing role that influences the condition.",,False
+RO.0003305,contributes to severity of condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity influences the severity with which a condition manifests in an individual.",,False
+RO.0003306,contributes to frequency of condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity influences the frequency of the condition in a population.",,False
+RO.0003307,ameliorates condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the presence of the entity reduces or eliminates some or all aspects of the condition.",,False
+RO.0003308,correlated with condition,A relationship between an entity and a condition (phenotype or disease) with which it exhibits a statistical dependence relationship.,,False
+RO.0003309,exacerbates condition,"A relationship between an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) and a condition (a phenotype or disease), where the presence of the entity worsens some or all aspects of the condition.",,False
+RO.0003310,condition ameliorated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are reduced or eliminated by the presence of the entity.",,False
+RO.0003311,condition exacerbated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are worsened by the presence of the entity.",,False
+RO.0004000,condition has genetic basis in,,RO.0004010,False
+RO.0004001,has material basis in gain of function germline mutation in,,RO.0004011,False
+RO.0004002,has material basis in loss of function germline mutation in,,RO.0004012,False
+RO.0004003,has material basis in germline mutation in,,RO.0004013,False
+RO.0004004,has material basis in somatic mutation in,,RO.0004014,False
+RO.0004005,has major susceptibility factor,,RO.0004015,False
+RO.0004006,has partial material basis in germline mutation in,,RO.0004016,False
+RO.0004007,has primary input or output,p 'has primary input ot output' c iff either (a) p 'has primary input' c or (b) p 'has primary output' c.,,False
+RO.0004008,has primary output,"p has primary output c if (a) p has output c and (b) the goal of process is to modify, produce, or transform c.",,False
+RO.0004009,has primary input,"p has primary input c if (a) p has input c and (b) the goal of process is to modify, consume, or transform c.",,False
+RO.0004010,is genetic basis for condition,,,False
+RO.0004011,is causal gain of function germline mutation of in,"Relates a gene to condition, such that a mutation in this gene in a germ cell provides a new function of the corresponding product and that is sufficient to produce the condition and that can be passed on to offspring[modified from orphanet].",,False
+RO.0004012,is causal loss of function germline mutation of in,"Relates a gene to condition, such that a mutation in this gene in a germ cell impairs the function of the corresponding product and that is sufficient to produce the condition and that can be passed on to offspring[modified from orphanet].",,False
+RO.0004013,is causal germline mutation in,"Relates a gene to condition, such that a mutation in this gene is sufficient to produce the condition and that can be passed on to offspring[modified from orphanet].",,False
+RO.0004014,is causal somatic mutation in,"Relates a gene to condition, such that a mutation in this gene is sufficient to produce the condition but that cannot be passed on to offspring[modified from orphanet].",,False
+RO.0004015,is causal susceptibility factor for,"Relates a gene to condition, such that a mutation in this gene predisposes to the development of a condition  and that is necessary but not sufficient to develop the condition[modified from orphanet].",,False
+RO.0004016,is causal germline mutation partially giving rise to,"Relates a gene to condition, such that a mutation in this gene partially contributes to the presentation of this condition[modified from orphanet].",,False
+RO.0004017,realizable has basis in,,RO.0004018,False
+RO.0004018,is basis for realizable,,,False
+RO.0004019,disease has basis in,,,False
+RO.0004020,disease has basis in dysfunction of,A relation that holds between the disease and a material entity where the physical basis of the disease is a disorder of that material entity that affects its function.,,False
+RO.0004021,disease has basis in disruption of,A relation that holds between the disease and a process where the physical basis of the disease disrupts execution of a key biological process.,,False
+RO.0004022,disease has basis in feature,A relation that holds between the disease and a feature (a phenotype or other disease) where the physical basis of the disease is the feature.,,False
+RO.0004023,causal relationship with disease as subject,,,False
+RO.0004024,disease causes disruption of,A relationship between a disease and a process where the disease process disrupts the execution of the process.,,False
+RO.0004025,disease causes dysfunction of,,,False
+RO.0004026,disease has location,A relationship between a disease and an anatomical entity where the disease has one or more features that are located in that entity.,,False
+RO.0004027,disease has inflammation site,A relationship between a disease and an anatomical entity where the disease is triggered by an inflammatory response to stimuli occurring in the anatomical entity,,False
+RO.0004028,realized in response to stimulus,A relationship between a realizable entity R (e.g. function or disposition) and a material entity M where R is realized in response to a process that has an input stimulus of M.,,False
+RO.0004029,disease has feature,"A relationship between a disease and some feature of that disease, where the feature is either a phenotype or an isolated disease.",,False
+RO.0004030,disease arises from alteration in structure,"A relationship between a disease and an anatomical structure where the material basis of the disease is some pathological change in the structure. Anatomical structure includes cellular and sub-cellular entities, such as chromosome and organelles.",,False
+RO.0004031,enables subfunction,"Holds between an entity and an process P where the entity enables some larger compound process, and that larger process has-part P.",,False
+RO.0004032,"acts upstream of or within, positive effect",,,False
+RO.0004033,"acts upstream of or within, negative effect",,,False
+RO.0004034,"acts upstream of, positive effect","c 'acts upstream of, positive effect' p if c is enables f, and f is causally upstream of p, and the direction of f is positive",,False
+RO.0004035,"acts upstream of, negative effect","c 'acts upstream of, negative effect' p if c is enables f, and f is causally upstream of p, and the direction of f is negative",,False
+RO.0004046,"causally upstream of or within, negative effect",,,False
+RO.0004047,"causally upstream of or within, positive effect",,,False
+RO.0004096,DEPRECATED inheres in,,,False
+RO.0004097,DEPRECATED bearer of,,,False
+RO.0007000,has driver,"A relation between two entities, in which one of the entities is any natural or human-influenced factor that directly or indirectly causes a change in the other entity.",,False
+RO.0007001,has disease driver,"A relation between an entity and a disease of a host, in which the entity is not part of the host itself, and the condition results in pathological processes.",,False
+RO.0008501,epiphyte of,An interaction relationship wherein a plant or algae is living on the outside surface of another plant.,RO.0008502,False
+RO.0008502,has epiphyte,inverse of epiphyte of,,False
+RO.0008503,kleptoparasite of,"A sub-relation of parasite of in which a parasite steals resources from another organism, usually food or nest material",RO.0008504,False
+RO.0008504,kleptoparasitized by,inverse of kleptoparasite of,,False
+RO.0008505,creates habitat for,An interaction relationship wherein one organism creates a structure or environment that is lived in by another organism.,,False
+RO.0008506,ecologically co-occurs with,An interaction relationship describing organisms that often occur together at the same time and space or in the same environment.,,True
+RO.0008507,lays eggs on,An interaction relationship in which organism a lays eggs on the outside surface of organism b. Organism b is neither helped nor harmed in the process of egg laying or incubation.,RO.0008508,False
+RO.0008508,has eggs laid on by,inverse of lays eggs on,,False
+RO.0008509,has roost,"x 'has roost' y if and only if: x is an organism, y is a habitat, and y can support rest behaviors x.",,False
+RO.0009001,has substance added,"""has substance added"" is a relation existing between a (physical) entity and a substance in which the entity has had the substance added to it at some point in time.",,False
+RO.0009002,has substance removed,"""has substance removed"" is a relation existing between two physical entities in which the first entity has had the second entity (a substance) removed from it at some point in time.",,False
+RO.0009003,immersed in,"""immersed in"" is a relation between a (physical) entity and a fluid substance in which the entity is wholely or substantially surrounded by the substance.",,False
+RO.0009004,has consumer,'has consumer' is a relation between a material entity and an organism in which the former can normally be digested or otherwise absorbed by the latter without immediate or persistent ill effect.,,False
+RO.0009005,has primary substance added,'has primary substance added' indicates that an entity has had the given substance added to it in a proportion greater than any other added substance.,,False
+RO.0009006,assay measures characteristic,"A relation between an assay and a characteristic, in which the assay generates a data item which is a measure of a characteristic.",RO.0009007,False
+RO.0009007,characteristic measured by assay,Inverse of 'assay measures characteristic',,False
+RO.0009501,realized in response to,"r 'realized in response to' s iff, r is a realizable (e.g. a plant trait such as responsivity to drought), s is an environmental stimulus (a process), and s directly causes the realization of r.",,False
+RO.0010001,generically depends on,A generically dependent continuant *b* generically depends on an independent continuant *c* at time *t* means: there inheres in *c* a specifically deendent continuant which concretizes *b* at *t*.,RO.0010002,False
+RO.0010002,is carrier of,*b* is carrier of *c* at time *t* if and only if *c* *g-depends on* *b* at *t*,,False
+RO.0011002,regulates activity of,"The entity A has an activity that regulates an activity of the entity B. For example, A and B are gene products where the catalytic activity of A regulates the kinase activity of B.",,False
+RO.0011003,regulates quantity of,The entity A has an activity that regulates the quantity or abundance or concentration of the entity B.,,False
+RO.0011004,indirectly regulates activity of,The entity A is not immediately upstream of the entity B but A has an activity that regulates an activity performed by B.,,False
 RO.0011007,decreases by repression quantity of,"The entity A has an activity that down-regulates by repression the quantity of B. The down-regulation is due to A having an effect on an intermediate entity (typically a DNA or mRNA element) which can produce B.
 
-For example, protein A (transcription factor) indirectly decreases by repression the quantity of protein B (gene product) if and only if A negatively regulates the process of transcription or translation of a nucleic acid element that produces B."
+For example, protein A (transcription factor) indirectly decreases by repression the quantity of protein B (gene product) if and only if A negatively regulates the process of transcription or translation of a nucleic acid element that produces B.",,False
 RO.0011008,increases by expression quantity of,"The entity A has an activity that up-regulates by expression the quantity of B. The up-regulation is due to A having an effect on an intermediate entity (typically a DNA or mRNA element) which can produce B.
 
-For example, protein A (transcription factor) indirectly increases by expression the quantity of protein B (gene product) if and only if A positively regulates the process of transcription or translation of a nucleic acid element that produces B."
-RO.0011009,directly positively regulates quantity of,The entity A has an activity that directly positively regulates the quantity of B.
-RO.0011010,directly negatively regulates quantity of,The entity A has an activity that directly negatively regulates the quantity of B.
-RO.0011013,indirectly positively regulates activity of,The entity A is not immediately upstream of the entity B and has an activity that up-regulates an activity performed by B.
-RO.0011014,destabilizes quantity of,An entity A directly interacts with B and A has an activity that decreases the amount of an entity B by degradating it.
-RO.0011015,stabilizes quantity of,An entity A physically interacts with B and A has an activity that increases the amount of an entity B by stabilizing it.
-RO.0011016,indirectly negatively regulates activity of,The entity A is not immediately upstream of the entity B and has an activity that down-regulates an activity performed by B.
-RO.0011021,directly regulates quantity of,"The entity A, immediately upstream of B, has an activity that directly regulates the quantity of B."
-RO.0011022,indirectly regulates quantity of,"The entity A is not immediately upstream of the entity B, but A has an activity that regulates the quantity or abundance or concentration of B."
-RO.0011023,indirectly negatively regulates quantity of,"The entity A does not physically interact with the entity B, and A has an activity that down-regulates the quantity or abundance or concentration of B."
-RO.0011024,indirectly positively regulates quantity of,"The entity A does not physically interact with the entity B, and A has an activity that up-regulates the quantity or abundance or concentration of B."
-RO.0012000,has small molecule regulator,"a relation between a process and a continuant, in which the process is regulated by the small molecule continuant"
-RO.0012001,has small molecule activator,"a relation between a process and a continuant, in which the process is activated by the small molecule continuant"
-RO.0012002,has small molecule inhibitor,"a relation between a process and a continuant, in which the process is inhibited by the small molecule continuant"
-RO.0012003,acts on population of,"p acts on population of c iff c' is a collection, has members of type c, and p has participant c"
-RO.0012004,is small molecule regulator of,"a relation between a continuant and a process, in which the continuant is a small molecule that regulates the process"
-RO.0012005,is small molecule activator of,"a relation between a continuant and a process, in which the continuant is a small molecule that activates the process"
-RO.0012006,is small molecule inhibitor of,"a relation between a continuant and a process, in which the continuant is a small molecule that inhibits the process"
-RO.0012007,results in adhesion of,"The relationship that links anatomical entities with a process that results in the adhesion of two or more entities via the non-covalent interaction of molecules expressed in, located in, and/or adjacent to, those entities."
-RO.0012008,results in fusion of,
-RO.0012009,constitutively upstream of,"p is constitutively upstream of q iff p is causally upstream of q, p is required for execution of q or a part of q, and the execution of p is approximately constant."
-RO.0012010,removes input for,"p removes input for q iff p is causally upstream of q, there exists some c such that p has_input c and q has_input c, p reduces the levels of c, and c is rate limiting for execution of q."
-RO.0012011,indirectly causally upstream of,p is indirectly causally upstream of q iff p is causally upstream of q and there exists some process r such that p is causally upstream of r and r is causally upstream of q.
-RO.0012012,indirectly regulates,p indirectly regulates q iff p is indirectly causally upstream of q and p regulates q.
-RO.0012013,positively regulates in another organism,"x 'positively regulates in other organism' y if and only if: (x is the realization of a function to increase the frequency, rate or extent of y) AND (the agents of x are produced by organism o1 and the agents of y are produced by organism o2)."
-RO.0012014,negatively regulates in another organism,"x 'negatively regulates in other organism' y if and only if: (x is the realization of a function to reduce the frequency, rate or extent of y) AND (the agents of x are produced by organism o1 and the agents of y are produced by organism o2)."
-RO.0013001,has synaptic IO in region,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input and/or output synapses in that region."
-RO.0013002,receives synaptic input in region,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input synapses in that region."
-RO.0013003,sends synaptic output to region,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of output synapses in that region."
-RO.0013004,has synaptic IO throughout,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input and/or output synapses distributed throughout that region (rather than confined to a subregion)."
-RO.0013005,receives synaptic input throughout,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input synapses distributed throughout that region (rather than confined to a subregion)."
-RO.0013006,sends synaptic output throughout,"A relationship between a neuron and a region, where the neuron has a functionally relevant number output synapses distributed throughout that region (rather than confined to a subregion)."
-RO.0013007,has sensory dendrite in,Relation between a sensory neuron and some structure in which it receives sensory input via a sensory dendrite.
-RO.0013008,receives synaptic input from neuron,A relationship between an anatomical structure (including cells) and a neuron that has a functionally relevant number of chemical synapses to it.
-RO.0013009,sends synaptic output to cell,A relationship between a neuron and a cell that it has a functionally relevant number of chemical synapses to.
-RO.0014001,disease has infectious agent,A relationship between a disease and an infectious agent where the material basis of the disease is an infection with some infectious agent.
-RO.0015001,has exemplar data,A relation between a material entity and some data in which the data is taken as exemplifying the material entity.
-RO.0015002,exemplar data of,
-RO.0015003,subcluster of,A relation between a group and another group it is part of but does not fully constitute.
-RO.0015004,has characterizing marker set,A relation that applies between a cell type and a set of markers that can be used to uniquely identify that cell type.
-RO.0015006,different in magnitude relative to,"q1 different_in_magnitude_relative_to q2 if and only if magnitude(q1) NOT =~ magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale."
-RO.0015007,increased in magnitude relative to,"q1 increased_in_magnitude_relative_to q2 if and only if magnitude(q1) > magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale."
-RO.0015008,decreased in magnitude relative to,"q1 decreased_in_magnitude_relative_to q2 if and only if magnitude(q1) < magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale."
-RO.0015009,similar in magnitude relative to,"q1 similar_in_magnitude_relative_to q2 if and only if magnitude(q1) =~ magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale."
-RO.0015010,has relative magnitude,
-RO.0015011,has cross section,"s3 has_cross_section s3 if and only if : there exists some 2d plane that intersects the bearer of s3, and the impression of s3 upon that plane has shape quality s2."
-RO.0015012,reciprocal of,q1 reciprocal_of q2 if and only if : q1 and q2 are relational qualities and a phenotype e q1 e2 mutually implies a phenotype e2 q2 e.
-RO.0015015,has high plasma membrane amount,A relation between a cell and molecule or complex such that every instance of the cell has a high number of instances of that molecule expressed on the cell surface.
-RO.0015016,has low plasma membrane amount,A relation between a cell and molecule or complex such that every instance of the cell has a low number of instances of that molecule expressed on the cell surface.
-RO.0016001,has phenotype or disease,
-RO.0016002,has disease,A relationship that holds between an organism and a disease. Here a disease is construed broadly as a disposition to undergo pathological processes that exists in an organism because of one or more disorders in that organism.
-RO.0016004,has exposure medium,"X has exposure medium Y if X is an exposure event (process), Y is a material entity, and the stimulus for X is transmitted or carried in Y."
-RO.0017001,device utilizes material,"X device utilizes material Y means X and Y are material entities, and X is capable of some process P that has input Y."
-RO.0017003,positively correlated with,A relation between entities in which one increases or decreases as the other does the same.
-RO.0017004,negatively correlated with,A relation between entities in which one increases as the other decreases.
-RO.0017005,contains measured amount,A relation between a container and measurement datum that specifies the actual amount of material in the container.
-RO.0017006,has maximum capacity,A relation that relates a container to a measurement datum that specifies the maximum capacity of the container. Capacity can refer to either weight or volume.
-RO.0017008,owns,A primitive relation that holds between entities x and y in which y is at x's full disposal.
-RO.0017009,is owned by,Inverse of the owns relation.
-RO.0018001,is myristoyltransferase activity,Helper relation for OWL definition of RO:0018002 myristoylates
-RO.0018002,myristoylates,"A molecularly-interacts-with relationship between two entities, where the subject catalyzes a myristoylation activity that takes the object as input"
-RO.0018003,myristoylated by,inverse of myristoylates
-RO.0018027,is agonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor activates some activity of the receptor
-RO.0018028,is inverse agonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor inhibits some activity of the receptor to below basal level
-RO.0018029,is antagonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor reduces some activity of the receptor to basal level
-RO.0018030,chemical relationship,
-RO.0018031,is direct conjugate base of,"A is a direct conjugate base of B if and only if A is chemical entity that is a Brønsted–Lowry Base (i.e., can receive a proton) and by receiving a particular proton transforms it into B."
-RO.0018032,is direct conjugate acid of,"A is a direct conjugate acid of B if and only if A is chemical entity that is a Brønsted–Lowry Acid (i.e., can give up a proton) and by removing a particular proton transforms it into B."
+For example, protein A (transcription factor) indirectly increases by expression the quantity of protein B (gene product) if and only if A positively regulates the process of transcription or translation of a nucleic acid element that produces B.",,False
+RO.0011009,directly positively regulates quantity of,The entity A has an activity that directly positively regulates the quantity of B.,,False
+RO.0011010,directly negatively regulates quantity of,The entity A has an activity that directly negatively regulates the quantity of B.,,False
+RO.0011013,indirectly positively regulates activity of,The entity A is not immediately upstream of the entity B and has an activity that up-regulates an activity performed by B.,,False
+RO.0011014,destabilizes quantity of,An entity A directly interacts with B and A has an activity that decreases the amount of an entity B by degradating it.,,False
+RO.0011015,stabilizes quantity of,An entity A physically interacts with B and A has an activity that increases the amount of an entity B by stabilizing it.,,False
+RO.0011016,indirectly negatively regulates activity of,The entity A is not immediately upstream of the entity B and has an activity that down-regulates an activity performed by B.,,False
+RO.0011021,directly regulates quantity of,"The entity A, immediately upstream of B, has an activity that directly regulates the quantity of B.",,False
+RO.0011022,indirectly regulates quantity of,"The entity A is not immediately upstream of the entity B, but A has an activity that regulates the quantity or abundance or concentration of B.",,False
+RO.0011023,indirectly negatively regulates quantity of,"The entity A does not physically interact with the entity B, and A has an activity that down-regulates the quantity or abundance or concentration of B.",,False
+RO.0011024,indirectly positively regulates quantity of,"The entity A does not physically interact with the entity B, and A has an activity that up-regulates the quantity or abundance or concentration of B.",,False
+RO.0012000,has small molecule regulator,"a relation between a process and a continuant, in which the process is regulated by the small molecule continuant",RO.0012004,False
+RO.0012001,has small molecule activator,"a relation between a process and a continuant, in which the process is activated by the small molecule continuant",RO.0012005,False
+RO.0012002,has small molecule inhibitor,"a relation between a process and a continuant, in which the process is inhibited by the small molecule continuant",RO.0012006,False
+RO.0012003,acts on population of,"p acts on population of c iff c' is a collection, has members of type c, and p has participant c",,False
+RO.0012004,is small molecule regulator of,"a relation between a continuant and a process, in which the continuant is a small molecule that regulates the process",,False
+RO.0012005,is small molecule activator of,"a relation between a continuant and a process, in which the continuant is a small molecule that activates the process",,False
+RO.0012006,is small molecule inhibitor of,"a relation between a continuant and a process, in which the continuant is a small molecule that inhibits the process",,False
+RO.0012007,results in adhesion of,"The relationship that links anatomical entities with a process that results in the adhesion of two or more entities via the non-covalent interaction of molecules expressed in, located in, and/or adjacent to, those entities.",,False
+RO.0012008,results in fusion of,,,False
+RO.0012009,constitutively upstream of,"p is constitutively upstream of q iff p is causally upstream of q, p is required for execution of q or a part of q, and the execution of p is approximately constant.",,False
+RO.0012010,removes input for,"p removes input for q iff p is causally upstream of q, there exists some c such that p has_input c and q has_input c, p reduces the levels of c, and c is rate limiting for execution of q.",,False
+RO.0012011,indirectly causally upstream of,p is indirectly causally upstream of q iff p is causally upstream of q and there exists some process r such that p is causally upstream of r and r is causally upstream of q.,,False
+RO.0012012,indirectly regulates,p indirectly regulates q iff p is indirectly causally upstream of q and p regulates q.,,False
+RO.0012013,positively regulates in another organism,"x 'positively regulates in other organism' y if and only if: (x is the realization of a function to increase the frequency, rate or extent of y) AND (the agents of x are produced by organism o1 and the agents of y are produced by organism o2).",,False
+RO.0012014,negatively regulates in another organism,"x 'negatively regulates in other organism' y if and only if: (x is the realization of a function to reduce the frequency, rate or extent of y) AND (the agents of x are produced by organism o1 and the agents of y are produced by organism o2).",,False
+RO.0013001,has synaptic IO in region,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input and/or output synapses in that region.",,False
+RO.0013002,receives synaptic input in region,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input synapses in that region.",,False
+RO.0013003,sends synaptic output to region,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of output synapses in that region.",,False
+RO.0013004,has synaptic IO throughout,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input and/or output synapses distributed throughout that region (rather than confined to a subregion).",,False
+RO.0013005,receives synaptic input throughout,"A relationship between a neuron and a region, where the neuron has a functionally relevant number of input synapses distributed throughout that region (rather than confined to a subregion).",,False
+RO.0013006,sends synaptic output throughout,"A relationship between a neuron and a region, where the neuron has a functionally relevant number output synapses distributed throughout that region (rather than confined to a subregion).",,False
+RO.0013007,has sensory dendrite in,Relation between a sensory neuron and some structure in which it receives sensory input via a sensory dendrite.,,False
+RO.0013008,receives synaptic input from neuron,A relationship between an anatomical structure (including cells) and a neuron that has a functionally relevant number of chemical synapses to it.,RO.0013009,False
+RO.0013009,sends synaptic output to cell,A relationship between a neuron and a cell that it has a functionally relevant number of chemical synapses to.,,False
+RO.0014001,disease has infectious agent,A relationship between a disease and an infectious agent where the material basis of the disease is an infection with some infectious agent.,,False
+RO.0015001,has exemplar data,A relation between a material entity and some data in which the data is taken as exemplifying the material entity.,RO.0015002,False
+RO.0015002,exemplar data of,,,False
+RO.0015003,subcluster of,A relation between a group and another group it is part of but does not fully constitute.,,False
+RO.0015004,has characterizing marker set,A relation that applies between a cell type and a set of markers that can be used to uniquely identify that cell type.,,False
+RO.0015006,different in magnitude relative to,"q1 different_in_magnitude_relative_to q2 if and only if magnitude(q1) NOT =~ magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale.",,False
+RO.0015007,increased in magnitude relative to,"q1 increased_in_magnitude_relative_to q2 if and only if magnitude(q1) > magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale.",,False
+RO.0015008,decreased in magnitude relative to,"q1 decreased_in_magnitude_relative_to q2 if and only if magnitude(q1) < magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale.",,False
+RO.0015009,similar in magnitude relative to,"q1 similar_in_magnitude_relative_to q2 if and only if magnitude(q1) =~ magnitude(q2). Here, magnitude(q) is a function that maps a quality to a unit-invariant scale.",,False
+RO.0015010,has relative magnitude,,,False
+RO.0015011,has cross section,"s3 has_cross_section s3 if and only if : there exists some 2d plane that intersects the bearer of s3, and the impression of s3 upon that plane has shape quality s2.",,False
+RO.0015012,reciprocal of,q1 reciprocal_of q2 if and only if : q1 and q2 are relational qualities and a phenotype e q1 e2 mutually implies a phenotype e2 q2 e.,,False
+RO.0015015,has high plasma membrane amount,A relation between a cell and molecule or complex such that every instance of the cell has a high number of instances of that molecule expressed on the cell surface.,,False
+RO.0015016,has low plasma membrane amount,A relation between a cell and molecule or complex such that every instance of the cell has a low number of instances of that molecule expressed on the cell surface.,,False
+RO.0016001,has phenotype or disease,,,False
+RO.0016002,has disease,A relationship that holds between an organism and a disease. Here a disease is construed broadly as a disposition to undergo pathological processes that exists in an organism because of one or more disorders in that organism.,,False
+RO.0016004,has exposure medium,"X has exposure medium Y if X is an exposure event (process), Y is a material entity, and the stimulus for X is transmitted or carried in Y.",,False
+RO.0017001,device utilizes material,"X device utilizes material Y means X and Y are material entities, and X is capable of some process P that has input Y.",,False
+RO.0017003,positively correlated with,A relation between entities in which one increases or decreases as the other does the same.,,True
+RO.0017004,negatively correlated with,A relation between entities in which one increases as the other decreases.,,True
+RO.0017005,contains measured amount,A relation between a container and measurement datum that specifies the actual amount of material in the container.,,False
+RO.0017006,has maximum capacity,A relation that relates a container to a measurement datum that specifies the maximum capacity of the container. Capacity can refer to either weight or volume.,,False
+RO.0017008,owns,A primitive relation that holds between entities x and y in which y is at x's full disposal.,RO.0017009,False
+RO.0017009,is owned by,Inverse of the owns relation.,,False
+RO.0018001,is myristoyltransferase activity,Helper relation for OWL definition of RO:0018002 myristoylates,,False
+RO.0018002,myristoylates,"A molecularly-interacts-with relationship between two entities, where the subject catalyzes a myristoylation activity that takes the object as input",RO.0018003,False
+RO.0018003,myristoylated by,inverse of myristoylates,,False
+RO.0018027,is agonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor activates some activity of the receptor,,False
+RO.0018028,is inverse agonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor inhibits some activity of the receptor to below basal level,,False
+RO.0018029,is antagonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor reduces some activity of the receptor to basal level,,False
+RO.0018030,chemical relationship,,,False
+RO.0018031,is direct conjugate base of,"A is a direct conjugate base of B if and only if A is chemical entity that is a Brønsted–Lowry Base (i.e., can receive a proton) and by receiving a particular proton transforms it into B.",RO.0018032,False
+RO.0018032,is direct conjugate acid of,"A is a direct conjugate acid of B if and only if A is chemical entity that is a Brønsted–Lowry Acid (i.e., can give up a proton) and by removing a particular proton transforms it into B.",,False
 RO.0018033,is deprotonated form of,"A is a deprotonated form of B if and only if A is chemical entity that is a Brønsted–Lowry Base (i.e., can receive a proton) and by adding some nonzero number of protons transforms it into B.
 
-This is a transitive relationship and follows this design pattern: https://oborel.github.io/obo-relations/direct-and-indirect-relations."
+This is a transitive relationship and follows this design pattern: https://oborel.github.io/obo-relations/direct-and-indirect-relations.",RO.0018034,False
 RO.0018034,is protonated form of,"A is a protonated form of B if and only if A is chemical entity that is a Brønsted–Lowry Acid (i.e., can give up a proton) and by removing some nonzero number of protons transforms it into B.
 
-This is a transitive relationship and follows this design pattern: https://oborel.github.io/obo-relations/direct-and-indirect-relations."
+This is a transitive relationship and follows this design pattern: https://oborel.github.io/obo-relations/direct-and-indirect-relations.",,False
 RO.0018035,is matched small molecular pair with,"A and B are a matched small molecular pair (MMP) if their chemical structures define by a single, relatively small, well-defined structural modification.
 
 While this is normally called ""matched molecular pair"" in the cheminformatics literaturel, it is labeled as ""matched small molecular pair"" so as to reduce confusion with peptides and other macromolecules, which are also referenced as ""molecules"" in some contexts.
 
 This relationship is symmetric, meaning if A is a MMP with B iff B is a MMP with A.
 
-This relationship is not transitive, meaning that A is a MMP with B and B is a MMP with C, then A is not necessarily an MMP with C."
+This relationship is not transitive, meaning that A is a MMP with B and B is a MMP with C, then A is not necessarily an MMP with C.",,True
 RO.0018036,is tautomer of,"Two chemicals are tautomers if they can be readily interconverted.
 
-This commonly refers to prototropy in which a hydrogen's position is changed, such as between ketones and enols. This is also often observed in heterocyclic rings, e.g., ones containing nitrogens and/or have aryl functional groups containing heteroatoms."
-RO.0018037,is substitutent group from,"Group A is a substituent group from Chemical B if A represents the functional part of A and includes information about where it is connected. A is not itself a chemical with a fully formed chemical graph, but is rather a partial graph with one or more connection points that can be used to attach to another chemical graph, typically as a functionalization."
+This commonly refers to prototropy in which a hydrogen's position is changed, such as between ketones and enols. This is also often observed in heterocyclic rings, e.g., ones containing nitrogens and/or have aryl functional groups containing heteroatoms.",,True
+RO.0018037,is substitutent group from,"Group A is a substituent group from Chemical B if A represents the functional part of A and includes information about where it is connected. A is not itself a chemical with a fully formed chemical graph, but is rather a partial graph with one or more connection points that can be used to attach to another chemical graph, typically as a functionalization.",,False
 RO.0018038,has functional parent,"Chemical A has functional parent Chemical B if there is chemical transformation through which chemical B can be produced from chemical A. 
 
-For example, the relationship between a salt and a freebased compound is a ""has functional parent"" relationship."
+For example, the relationship between a salt and a freebased compound is a ""has functional parent"" relationship.",,False
 RO.0018039,is enantiomer of,"Chemicals A and B are enantiomers if they share the same molecular graph except the change of the configuration of substituents around exactly one chiral center.
 
-A chemical with no chiral centers can not have an enantiomer. A chemical with multiple chiral centers can have multiple enantiomers, but its enantiomers are not themselves enantiomers (they are diastereomers)."
-RO.0018040,has parent hydride,Chemical A has parent hydride Chemical B if there exists a molecular graphical transformation where functional groups on A are replaced with hydrogens in order to yield B.
-RO.0019000,regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) regulates characteristic (C) iff: P results in the existence of C OR affects the intensity or magnitude of C.
-RO.0019001,positively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) positively regulates characteristic (C) iff: P results in an increase in the intensity or magnitude of C.
-RO.0019002,negatively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) negatively regulates characteristic (C) iff: P results in a decrease in the intensity or magnitude of C.
-RO.0019501,confers susceptibility to condition,"Relates a gene to condition, such that a variation in this gene predisposes to the development of a condition."
-RO.0020101,vessel supplies blood to,x vessel supplies blood to y if and only if x is a vessel that supplies blood directly or indirectly to an anatomical structure y.
-RO.0020102,vessel drains blood from,x vessel drains blood from y if and only if x is a vessel that drains blood directly or indirectly from an anatomical structure y.
-RO.0020103,directly supplies and drains,"x directly supplies and drains y if and only if x is a capillary, y is an anatomical entity, and x directly supplies and drains y."
-RO.0020202,has numerator,A relationship between a ratio or proportion and its dividend.
-RO.0020203,has denominator,A relationship between a ratio or proportion and its divisor.
-RO.0040035,disease relationship,This relation groups  relations between diseases and any other kind of entity.
-RO.0040036,results in changes to anatomical or cellular structure,"p has anatomical participant c iff p has participant c, and c is an anatomical entity"
-RO.HOM0000000,in similarity relationship with,Relation between biological objects that resemble or are related to each other sufficiently to warrant a comparison.
-RO.HOM0000001,in homology relationship with,Similarity that results from common evolutionary origin.
-RO.HOM0000002,in homoplasy relationship with,Similarity that results from independent evolution.
-RO.HOM0000003,in homocracy relationship with,Similarity that is characterized by the organization of anatomical structures through the expression of homologous or identical patterning genes.
-RO.HOM0000004,in convergence relationship with,Homoplasy that involves different underlying mechanisms or structures.
-RO.HOM0000005,in parallelism relationship with,Homoplasy that involves homologous underlying mechanisms or structures.
-RO.HOM0000006,in structural homology relationship with,Homology that is defined by similarity with regard to selected structural parameters.
-RO.HOM0000007,in historical homology relationship with,Homology that is defined by common descent.
-RO.HOM0000008,in biological homology relationship with,"Homology that is defined by sharing of a set of developmental constraints, caused by locally acting self-regulatory mechanisms of differentiation, between individualized parts of the phenotype."
-RO.HOM0000009,in reversal relationship with,Homoplasy that involves phenotypes similar to those seen in ancestors within the lineage.
-RO.HOM0000010,in syntenic homology relationship with,Structural homology that is detected by similarity in content and organization between chromosomes.
-RO.HOM0000011,in paralogy relationship with,Historical homology that involves genes that diverged after a duplication event.
-RO.HOM0000012,in syntenic paralogy relationship with,Paralogy that involves sets of syntenic blocks.
-RO.HOM0000013,in syntenic orthology relationship with,Syntenic homology that involves chromosomes of different species.
-RO.HOM0000014,in partial homology relationship with,Structural homology that involves complex structures from which only a fraction of the elements that can be isolated are separately homologous.
-RO.HOM0000015,in protein structural homology relationship with,"Structural homology that is detected at the level of the 3D protein structure, but maybe not at the level of the amino acid sequence."
-RO.HOM0000016,in non functional homology relationship with,Structural homology that involves a pseudogenic feature and its functional ancestor.
-RO.HOM0000017,in orthology relationship with,Historical homology that involves genes that diverged after a speciation event.
-RO.HOM0000018,in xenology relationship with,Historical homology that is characterized by an interspecies (horizontal) transfer since the common ancestor.
-RO.HOM0000019,in 1 to 1 homology relationship with,Historical homology that involves two members sharing no other homologs in the lineages considered.
-RO.HOM0000020,in 1 to 1 orthology relationship with,Orthology that involves two genes that did not experience any duplication after the speciation event that created them.
-RO.HOM0000022,in ohnology relationship with,Paralogy that results from a whole genome duplication event.
-RO.HOM0000023,in in-paralogy relationship with,Paralogy that results from a lineage-specific duplication subsequent to a given speciation event.
-RO.HOM0000024,in out-paralogy relationship with,Paralogy that results from a duplication preceding a given speciation event.
-RO.HOM0000025,in pro-orthology relationship with,"1:many orthology that involves a gene in species A and one of its ortholog in species B, when duplications more recent than the species split have occurred in species B but not in species A."
-RO.HOM0000026,in semi-orthology relationship with,"1:many orthology that involves a gene in species A and its ortholog in species B, when duplications more recent than the species split have occurred in species A but not in species B."
-RO.HOM0000027,in serial homology relationship with,Iterative homology that involves structures arranged along the main body axis.
-RO.HOM0000028,in heterochronous homology relationship with,"Biological homology that is characterized by changes, over evolutionary time, in the rate or timing of developmental events of homologous structures."
-RO.HOM0000029,in paedomorphorsis relationship with,Heterochronous homology that is produced by a retention in adults of a species of traits previously seen only in juveniles.
-RO.HOM0000030,in peramorphosis relationship with,"Heterochronous homology that is produced by a maturation of individuals of a species past adulthood, which take on hitherto unseen traits."
-RO.HOM0000031,in progenesis relationship with,Paedomorphosis that is produced by precocious sexual maturation of an organism still in a morphologically juvenile stage.
-RO.HOM0000032,in neoteny relationship with,Paedomorphosis that is produced by a retardation of somatic development.
-RO.HOM0000033,in mimicry relationship with,Convergence that results from co-evolution usually involving an evolutionary arms race.
-RO.HOM0000034,in 1 to many orthology relationship with,Orthology that involves two genes when duplications more recent than the species split have occurred in one species but not the other.
-RO.HOM0000036,in many to many homology relationship with,Historical homology that involves two members of a larger set of homologs.
-RO.HOM0000037,in 1 to many homology relationship with,"Historical homology that involves a structure that has no other homologs in the species in which it is defined, and several homologous structures in another species."
-RO.HOM0000042,in apomorphy relationship with,"Historical homology that is based on recent shared ancestry, characterizing a monophyletic group."
-RO.HOM0000043,in plesiomorphy relationship with,Historical homology that is based on distant shared ancestry.
-RO.HOM0000044,in deep homology relationship with,Homocracy that involves morphologically and phylogenetically disparate structures that are the result of parallel evolution.
-RO.HOM0000045,in hemiplasy relationship with,Historical homology that is characterized by topological discordance between a gene tree and a species tree attributable to the phylogenetic sorting of genetic polymorphisms across successive nodes in a species tree.
-RO.HOM0000046,in gametology relationship with,Historical homology that involves not recombining and subsequently differentiated sex chromosomes.
-RO.HOM0000047,in chromosomal homology relationship with,Historical homology that involves the chromosomes able to pair (synapse) during meiosis.
-RO.HOM0000048,in many to many orthology relationship with,Orthology that involves two genes that experienced duplications more recent than the species split that created them.
-RO.HOM0000049,in within-species paralogy relationship with,Paralogy that involves genes from the same species.
-RO.HOM0000050,in between-species paralogy relationship with,Paralogy that involves genes from different species.
-RO.HOM0000051,in postdisplacement relationship with,Paedomorphosis that is produced by delayed growth of immature structures into the adult form.
-RO.HOM0000052,in hypermorphosis relationship with,Peramorphosis that is produced by a delay in the offset of development.
-RO.HOM0000053,in synology relationship with,"Xenology that results, not from the transfer of a gene between two species, but from a hybridization of two species."
-RO.HOM0000054,in isoorthology relationship with,Orthology that involves functional equivalent genes with retention of the ancestral function.
-RO.HOM0000055,in tandem paralogy relationship with,Paralogy that is characterized by duplication of adjacent sequences on a chromosome segment.
-RO.HOM0000057,in latent homology relationship with,"Parallelism that involves morphologically very similar structures, occurring only within some members of a taxon and absent in the common ancestor (which possessed the developmental basis to develop this character)."
-RO.HOM0000058,in syngeny relationship with,"Homocracy that involves recognizably corresponding characters that occurs in two or more taxa, or as a repeated unit within an individual."
-RO.HOM0000060,in apparent orthology relationship with,Between-species paralogy that involves single copy paralogs resulting from reciprocal gene loss.
-RO.HOM0000061,in pseudoparalogy relationship with,Xenology that involves genes that ended up in a given genome as a result of a combination of vertical inheritance and horizontal gene transfer.
-RO.HOM0000062,in equivalogy relationship with,Historical homology that involves functional equivalent genes with retention of the ancestral function.
-RO.HOM0000063,in interology relationship with,Historical homology that involves orthologous pairs of interacting molecules in different organisms.
-RO.HOM0000065,in functional equivalence relationship with,Similarity that is characterized by interchangeability in function.
-RO.HOM0000066,in iterative homology relationship with,Biological homology that involves parts of the same organism.
-RO.HOM0000068,in paraxenology relationship with,"Xenology that is characterized by multiple horizontal transfer events, resulting in the presence of two or more copies of the foreign gene in the host genome."
-RO.HOM0000069,in plerology relationship with,Paralogy that is characterized by extra similarity between paralogous sequences resulting from concerted evolution.
-RO.HOM0000071,in homotopy relationship with,Structural homology that involves structures with the same or similar relative positions.
-RO.HOM0000072,in homeosis relationship with,Biological homology that involves an ectopic structure and the normally positioned structure.
-RO.HOM0000073,in homoeology relationship with,Synology that results from allopolyploidy.
-RO.HOM0000074,in paramorphism relationship with,"Iterative homology that involves two structures, one of which originated as a duplicate of the other and co-opted the expression of patterning genes of the ancestral structure."
-RO.HOM0000075,in regulogy relationship with,Historical homology that involves orthologous pairs of transcription factors and downstream regulated genes in different organisms.
-envo#has.increased.levels.of,,
+A chemical with no chiral centers can not have an enantiomer. A chemical with multiple chiral centers can have multiple enantiomers, but its enantiomers are not themselves enantiomers (they are diastereomers).",,True
+RO.0018040,has parent hydride,Chemical A has parent hydride Chemical B if there exists a molecular graphical transformation where functional groups on A are replaced with hydrogens in order to yield B.,,False
+RO.0019000,regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) regulates characteristic (C) iff: P results in the existence of C OR affects the intensity or magnitude of C.,,False
+RO.0019001,positively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) positively regulates characteristic (C) iff: P results in an increase in the intensity or magnitude of C.,,False
+RO.0019002,negatively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) negatively regulates characteristic (C) iff: P results in a decrease in the intensity or magnitude of C.,,False
+RO.0019501,confers susceptibility to condition,"Relates a gene to condition, such that a variation in this gene predisposes to the development of a condition.",,False
+RO.0020101,vessel supplies blood to,x vessel supplies blood to y if and only if x is a vessel that supplies blood directly or indirectly to an anatomical structure y.,,False
+RO.0020102,vessel drains blood from,x vessel drains blood from y if and only if x is a vessel that drains blood directly or indirectly from an anatomical structure y.,,False
+RO.0020103,directly supplies and drains,"x directly supplies and drains y if and only if x is a capillary, y is an anatomical entity, and x directly supplies and drains y.",,False
+RO.0020202,has numerator,A relationship between a ratio or proportion and its dividend.,,False
+RO.0020203,has denominator,A relationship between a ratio or proportion and its divisor.,,False
+RO.0040035,disease relationship,This relation groups  relations between diseases and any other kind of entity.,,False
+RO.0040036,results in changes to anatomical or cellular structure,"p has anatomical participant c iff p has participant c, and c is an anatomical entity",,False
+RO.HOM0000000,in similarity relationship with,Relation between biological objects that resemble or are related to each other sufficiently to warrant a comparison.,,True
+RO.HOM0000001,in homology relationship with,Similarity that results from common evolutionary origin.,,True
+RO.HOM0000002,in homoplasy relationship with,Similarity that results from independent evolution.,,True
+RO.HOM0000003,in homocracy relationship with,Similarity that is characterized by the organization of anatomical structures through the expression of homologous or identical patterning genes.,,True
+RO.HOM0000004,in convergence relationship with,Homoplasy that involves different underlying mechanisms or structures.,,True
+RO.HOM0000005,in parallelism relationship with,Homoplasy that involves homologous underlying mechanisms or structures.,,True
+RO.HOM0000006,in structural homology relationship with,Homology that is defined by similarity with regard to selected structural parameters.,,True
+RO.HOM0000007,in historical homology relationship with,Homology that is defined by common descent.,,True
+RO.HOM0000008,in biological homology relationship with,"Homology that is defined by sharing of a set of developmental constraints, caused by locally acting self-regulatory mechanisms of differentiation, between individualized parts of the phenotype.",,True
+RO.HOM0000009,in reversal relationship with,Homoplasy that involves phenotypes similar to those seen in ancestors within the lineage.,,True
+RO.HOM0000010,in syntenic homology relationship with,Structural homology that is detected by similarity in content and organization between chromosomes.,,True
+RO.HOM0000011,in paralogy relationship with,Historical homology that involves genes that diverged after a duplication event.,,True
+RO.HOM0000012,in syntenic paralogy relationship with,Paralogy that involves sets of syntenic blocks.,,True
+RO.HOM0000013,in syntenic orthology relationship with,Syntenic homology that involves chromosomes of different species.,,True
+RO.HOM0000014,in partial homology relationship with,Structural homology that involves complex structures from which only a fraction of the elements that can be isolated are separately homologous.,,True
+RO.HOM0000015,in protein structural homology relationship with,"Structural homology that is detected at the level of the 3D protein structure, but maybe not at the level of the amino acid sequence.",,True
+RO.HOM0000016,in non functional homology relationship with,Structural homology that involves a pseudogenic feature and its functional ancestor.,,True
+RO.HOM0000017,in orthology relationship with,Historical homology that involves genes that diverged after a speciation event.,,True
+RO.HOM0000018,in xenology relationship with,Historical homology that is characterized by an interspecies (horizontal) transfer since the common ancestor.,,True
+RO.HOM0000019,in 1 to 1 homology relationship with,Historical homology that involves two members sharing no other homologs in the lineages considered.,,True
+RO.HOM0000020,in 1 to 1 orthology relationship with,Orthology that involves two genes that did not experience any duplication after the speciation event that created them.,,True
+RO.HOM0000022,in ohnology relationship with,Paralogy that results from a whole genome duplication event.,,True
+RO.HOM0000023,in in-paralogy relationship with,Paralogy that results from a lineage-specific duplication subsequent to a given speciation event.,,True
+RO.HOM0000024,in out-paralogy relationship with,Paralogy that results from a duplication preceding a given speciation event.,,True
+RO.HOM0000025,in pro-orthology relationship with,"1:many orthology that involves a gene in species A and one of its ortholog in species B, when duplications more recent than the species split have occurred in species B but not in species A.",,True
+RO.HOM0000026,in semi-orthology relationship with,"1:many orthology that involves a gene in species A and its ortholog in species B, when duplications more recent than the species split have occurred in species A but not in species B.",,True
+RO.HOM0000027,in serial homology relationship with,Iterative homology that involves structures arranged along the main body axis.,,True
+RO.HOM0000028,in heterochronous homology relationship with,"Biological homology that is characterized by changes, over evolutionary time, in the rate or timing of developmental events of homologous structures.",,True
+RO.HOM0000029,in paedomorphorsis relationship with,Heterochronous homology that is produced by a retention in adults of a species of traits previously seen only in juveniles.,,True
+RO.HOM0000030,in peramorphosis relationship with,"Heterochronous homology that is produced by a maturation of individuals of a species past adulthood, which take on hitherto unseen traits.",,True
+RO.HOM0000031,in progenesis relationship with,Paedomorphosis that is produced by precocious sexual maturation of an organism still in a morphologically juvenile stage.,,True
+RO.HOM0000032,in neoteny relationship with,Paedomorphosis that is produced by a retardation of somatic development.,,True
+RO.HOM0000033,in mimicry relationship with,Convergence that results from co-evolution usually involving an evolutionary arms race.,,True
+RO.HOM0000034,in 1 to many orthology relationship with,Orthology that involves two genes when duplications more recent than the species split have occurred in one species but not the other.,,True
+RO.HOM0000036,in many to many homology relationship with,Historical homology that involves two members of a larger set of homologs.,,True
+RO.HOM0000037,in 1 to many homology relationship with,"Historical homology that involves a structure that has no other homologs in the species in which it is defined, and several homologous structures in another species.",,True
+RO.HOM0000042,in apomorphy relationship with,"Historical homology that is based on recent shared ancestry, characterizing a monophyletic group.",,True
+RO.HOM0000043,in plesiomorphy relationship with,Historical homology that is based on distant shared ancestry.,,True
+RO.HOM0000044,in deep homology relationship with,Homocracy that involves morphologically and phylogenetically disparate structures that are the result of parallel evolution.,,True
+RO.HOM0000045,in hemiplasy relationship with,Historical homology that is characterized by topological discordance between a gene tree and a species tree attributable to the phylogenetic sorting of genetic polymorphisms across successive nodes in a species tree.,,True
+RO.HOM0000046,in gametology relationship with,Historical homology that involves not recombining and subsequently differentiated sex chromosomes.,,True
+RO.HOM0000047,in chromosomal homology relationship with,Historical homology that involves the chromosomes able to pair (synapse) during meiosis.,,True
+RO.HOM0000048,in many to many orthology relationship with,Orthology that involves two genes that experienced duplications more recent than the species split that created them.,,True
+RO.HOM0000049,in within-species paralogy relationship with,Paralogy that involves genes from the same species.,,True
+RO.HOM0000050,in between-species paralogy relationship with,Paralogy that involves genes from different species.,,True
+RO.HOM0000051,in postdisplacement relationship with,Paedomorphosis that is produced by delayed growth of immature structures into the adult form.,,True
+RO.HOM0000052,in hypermorphosis relationship with,Peramorphosis that is produced by a delay in the offset of development.,,True
+RO.HOM0000053,in synology relationship with,"Xenology that results, not from the transfer of a gene between two species, but from a hybridization of two species.",,True
+RO.HOM0000054,in isoorthology relationship with,Orthology that involves functional equivalent genes with retention of the ancestral function.,,True
+RO.HOM0000055,in tandem paralogy relationship with,Paralogy that is characterized by duplication of adjacent sequences on a chromosome segment.,,True
+RO.HOM0000057,in latent homology relationship with,"Parallelism that involves morphologically very similar structures, occurring only within some members of a taxon and absent in the common ancestor (which possessed the developmental basis to develop this character).",,True
+RO.HOM0000058,in syngeny relationship with,"Homocracy that involves recognizably corresponding characters that occurs in two or more taxa, or as a repeated unit within an individual.",,True
+RO.HOM0000060,in apparent orthology relationship with,Between-species paralogy that involves single copy paralogs resulting from reciprocal gene loss.,,True
+RO.HOM0000061,in pseudoparalogy relationship with,Xenology that involves genes that ended up in a given genome as a result of a combination of vertical inheritance and horizontal gene transfer.,,True
+RO.HOM0000062,in equivalogy relationship with,Historical homology that involves functional equivalent genes with retention of the ancestral function.,,True
+RO.HOM0000063,in interology relationship with,Historical homology that involves orthologous pairs of interacting molecules in different organisms.,,True
+RO.HOM0000065,in functional equivalence relationship with,Similarity that is characterized by interchangeability in function.,,True
+RO.HOM0000066,in iterative homology relationship with,Biological homology that involves parts of the same organism.,,True
+RO.HOM0000068,in paraxenology relationship with,"Xenology that is characterized by multiple horizontal transfer events, resulting in the presence of two or more copies of the foreign gene in the host genome.",,True
+RO.HOM0000069,in plerology relationship with,Paralogy that is characterized by extra similarity between paralogous sequences resulting from concerted evolution.,,True
+RO.HOM0000071,in homotopy relationship with,Structural homology that involves structures with the same or similar relative positions.,,True
+RO.HOM0000072,in homeosis relationship with,Biological homology that involves an ectopic structure and the normally positioned structure.,,True
+RO.HOM0000073,in homoeology relationship with,Synology that results from allopolyploidy.,,True
+RO.HOM0000074,in paramorphism relationship with,"Iterative homology that involves two structures, one of which originated as a duplicate of the other and co-opted the expression of patterning genes of the ancestral structure.",,True
+RO.HOM0000075,in regulogy relationship with,Historical homology that involves orthologous pairs of transcription factors and downstream regulated genes in different organisms.,,True
+envo#has.increased.levels.of,,,,False
diff --git a/data/derived/properties.synthesis.2.csv b/data/derived/properties.synthesis.2.csv
index 8980023..d530122 100644
--- a/data/derived/properties.synthesis.2.csv
+++ b/data/derived/properties.synthesis.2.csv
@@ -34,11 +34,11 @@ PEL.000026,produces,cause (a particular result or situation) to happen or exist,
 PEL.000027,promotes,,
 PEL.000028,provokes,call forth an action,
 PEL.000029,reduces,"diminish in size, amount, extent, or number",
-PEL.000030,repress,inactivate (a gene or formation of a gene product),
+PEL.000030,represses,inactivate (a gene or formation of a gene product),
 PEL.000031,reverts,return to previous state,
 PEL.000032,secretes,"(of a cell, gland, or organ) produce and discharge (a substance)",insulin is secreted in response to rising levels of glucose in the blood
 PEL.000033,shuts,cause to cease or suspend an operation or activity,
-PEL.000035,suppress,inhibit the genetic expression of,
+PEL.000035,suppresses,inhibit the genetic expression of,
 PEL.000036,triggers,initiate or set off,
 PEL.000037,upregulates,"increase the rate or level of (a process such as gene expression, or sensitivity to a physiologically active substance) by upregulation",
 RO.0000053,has characteristic,Inverse of characteristic_of,
@@ -338,3 +338,40 @@ RO.0019001,positively regulates characteristic,A relationship that holds between
 RO.0019002,negatively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) negatively regulates characteristic (C) iff: P results in a decrease in the intensity or magnitude of C.,
 RO.0020202,has numerator,A relationship between a ratio or proportion and its dividend.,
 RO.0020203,has denominator,A relationship between a ratio or proportion and its divisor.,
+PEL.000020,interrupts,stop the continuous progress of (an activity or process),
+PEL.000034,stimulates,excite to activity or growth or to greater activity transactivate,
+PEL.000038,activated by,converse of 'activates': X is activated by Y when Y activates X,
+PEL.000039,actuated by,converse of 'actuates': X is actuated by Y when Y actuates X,
+PEL.000040,associated with,converse of 'associates': X is associated with Y when Y associates X,
+PEL.000041,bound by,converse of 'binds to': X is bound by Y when Y binds to X,
+PEL.000042,caused by,converse of 'causes': X is caused by Y when Y causes X,
+PEL.000043,constrained by,converse of 'constrains': X is constrained by Y when Y constrains X,
+PEL.000044,controlled by,converse of 'controls': X is controlled by Y when Y controls X,
+PEL.000045,degraded by,converse of 'degrades': X is degraded by Y when Y degrades X,
+PEL.000046,destroyed by,converse of 'destroys': X is destroyed by Y when Y destroys X,
+PEL.000047,directed by,converse of 'directs': X is directed by Y when Y directs X,
+PEL.000048,downregulated by,converse of 'downregulates': X is downregulated by Y when Y downregulates X,
+PEL.000049,enhanced by,converse of 'enhances': X is enhanced by Y when Y enhances X,
+PEL.000050,facilitated by,converse of 'facilitates': X is facilitated by Y when Y facilitates X,
+PEL.000051,forced by,converse of 'forces': X is forced by Y when Y forces X,
+PEL.000052,hindered by,converse of 'hinders': X is hindered by Y when Y hinders X,
+PEL.000053,inactivated by,converse of 'inactivates': X is inactivated by Y when Y inactivates X,
+PEL.000054,increased by,converse of 'increases': X is increased by Y when Y increases X,
+PEL.000055,induced by,converse of 'induces': X is induced by Y when Y induces X,
+PEL.000056,inhibited by,converse of 'inhibits': X is inhibited by Y when Y inhibits X,
+PEL.000057,initiated by,converse of 'initiates': X is initiated by Y when Y initiates X,
+PEL.000058,interrupted by,converse of 'interrupts': X is interrupted by Y when Y interrupts X,
+PEL.000059,limited by,converse of 'limits': X is limited by Y when Y limits X,
+PEL.000060,mentioned by,converse of 'mentions': X is mentioned by Y when Y mentions X,
+PEL.000061,overproduced by,converse of 'overproduces': X is overproduced by Y when Y overproduces X,
+PEL.000062,phosphorylated by,converse of 'phosphorylates': X is phosphorylated by Y when Y phosphorylates X,
+PEL.000063,promoted by,converse of 'promotes': X is promoted by Y when Y promotes X,
+PEL.000064,provoked by,converse of 'provokes': X is provoked by Y when Y provokes X,
+PEL.000065,reduced by,converse of 'reduces': X is reduced by Y when Y reduces X,
+PEL.000066,repressed by,converse of 'represses': X is repressed by Y when Y represses X,
+PEL.000067,reverted by,converse of 'reverts': X is reverted by Y when Y reverts X,
+PEL.000068,secreted by,converse of 'secretes': X is secreted by Y when Y secretes X,
+PEL.000069,stimulated by,converse of 'stimulates': X is stimulated by Y when Y stimulates X,
+PEL.000070,suppressed by,converse of 'suppresses': X is suppressed by Y when Y suppresses X,
+PEL.000071,triggered by,converse of 'triggers': X is triggered by Y when Y triggers X,
+PEL.000072,upregulated by,converse of 'upregulates': X is upregulated by Y when Y upregulates X,
diff --git a/data/derived/properties.synthesis.2.inverse.csv b/data/derived/properties.synthesis.2.inverse.csv
new file mode 100644
index 0000000..98ac089
--- /dev/null
+++ b/data/derived/properties.synthesis.2.inverse.csv
@@ -0,0 +1,377 @@
+entity_id,label,description,inverse_entity_id,example
+BFO.0000051,has part,a core relation that holds between a whole and its part,,
+BFO.0000054,realized in,,BFO.0000055,
+BFO.0000055,realizes,,,
+BFO.0000062,preceded by,"x is preceded by y if and only if the time point at which y ends is before or equivalent to the time point at which x starts. Formally: x preceded by y iff ω(y) <= α(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",BFO.0000063,
+BFO.0000063,precedes,"x precedes y if and only if the time point at which x ends is before or equivalent to the time point at which y starts. Formally: x precedes y iff ω(x) <= α(y), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,
+BFO.0000066,occurs in,b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t,BFO.0000067,
+BFO.0000067,contains process,[copied from inverse property 'occurs in'] b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t,,
+PEL.000000,activates,"convert (something, such as a provitamin) into a biologically active derivative",,
+PEL.000001,actuates,cause to act in a particular way; motivate,,
+PEL.000002,associates,combine or join with other parts,,
+PEL.000003,binds to,"to combine with, form a bond with, or be taken up by a chemical or chemical structure",," ""An enzyme is structured in such a way as to be able to bind with its substrate"""
+PEL.000004,causes,make happen,,
+PEL.000005,constrains,"severely restrict the scope, extent, or activity of",,
+PEL.000006,controls,determine the behaviour or supervise the running of,,
+PEL.000007,degrades,break down or deteriorate chemically,,
+PEL.000008,destroys,end the existence of something by damaging or attacking it,,
+PEL.000009,directs,control the operations of; manage or govern,,
+PEL.000010,downregulates,"lower the rate or level of (a process such as gene expression, or sensitivity to a physiologically active substance) by downregulation",,
+PEL.000011,enhances,"intensify, increase, or further improve the quality",,
+PEL.000012,facilitates,make (an action or process) easy or easier,,
+PEL.000013,forces,make something happen,,
+PEL.000014,hinders,make it difficult for something to happen,,
+PEL.000015,inactivates,make inactive or inoperative,,
+PEL.000016,increases,"become or make greater in size, amount, or degree",,
+PEL.000017,induces,bring about or give rise to,,none of these measures induced a change of policy
+PEL.000018,inhibits,"hinder, restrain, or prevent (an action or process)",,
+PEL.000019,initiates,cause (a process or action) to begin,,he proposes to initiate discussions on planning procedures
+PEL.000022,limits,curtail or reduce in quantity or extent,,
+PEL.000023,mentions,refer to something,,
+PEL.000024,overproduces,produce an excessive amount,,"if soils are too fertile, the vines can overproduce, leading to vegetal aromas and flavors"
+PEL.000025,phosphorylates,introduce a phosphate group into (a molecule or compound),,
+PEL.000026,produces,cause (a particular result or situation) to happen or exist,,
+PEL.000027,promotes,,,
+PEL.000028,provokes,call forth an action,,
+PEL.000029,reduces,"diminish in size, amount, extent, or number",,
+PEL.000030,represses,inactivate (a gene or formation of a gene product),,
+PEL.000031,reverts,return to previous state,,
+PEL.000032,secretes,"(of a cell, gland, or organ) produce and discharge (a substance)",,insulin is secreted in response to rising levels of glucose in the blood
+PEL.000033,shuts,cause to cease or suspend an operation or activity,,
+PEL.000035,suppresses,inhibit the genetic expression of,,
+PEL.000036,triggers,initiate or set off,,
+PEL.000037,upregulates,"increase the rate or level of (a process such as gene expression, or sensitivity to a physiologically active substance) by upregulation",,
+RO.0000053,has characteristic,Inverse of characteristic_of,,
+RO.0000056,participates in,"a relation between a continuant and a process, in which the continuant is somehow involved in the process",RO.0000057,
+RO.0000057,has participant,"a relation between a process and a continuant, in which the continuant is somehow involved in the process",,
+RO.0000058,is concretized as,"A relationship between a generically dependent continuant and a specifically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. A generically dependent continuant may be concretized as multiple specifically dependent continuants.",RO.0000059,
+RO.0000059,concretizes,"A relationship between a specifically dependent continuant and a generically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. Multiple specifically dependent continuants can concretize the same generically dependent continuant.",,
+RO.0000085,has function,"a relation between an independent continuant (the bearer) and a function, in which the function specifically depends on the bearer for its existence",,
+RO.0000086,has quality,"a relation between an independent continuant (the bearer) and a quality, in which the quality specifically depends on the bearer for its existence",,
+RO.0000087,has role,"a relation between an independent continuant (the bearer) and a role, in which the role specifically depends on the bearer for its existence",,
+RO.0000091,has disposition,"a relation between an independent continuant (the bearer) and a disposition, in which the disposition specifically depends on the bearer for its existence",,
+RO.0001000,derives from,"a relation between two distinct material entities, the new entity and the old entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity",RO.0001001,
+RO.0001001,derives into,"a relation between two distinct material entities, the old entity and the new entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity",,
+RO.0001022,has allergic trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is not part of the host, and is considered harmless to non-allergic hosts, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",,
+RO.0001023,has autoimmune trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is part of the host itself, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",,
+RO.0001025,located in,"a relation between two independent continuants, the target and the location, in which the target is entirely within the location",,
+RO.0002001,aligned with,,,
+RO.0002002,has 2D boundary,"a relation between a material entity and a 2D immaterial entity (the boundary), in which the boundary delimits the material entity",,
+RO.0002004,tracheates,The relationship that holds between a trachea or tracheole and an antomical structure that is contained in (and so provides an oxygen supply to).,,
+RO.0002007,bounding layer of,"X outer_layer_of Y iff:
+. X :continuant that bearer_of some PATO:laminar
+. X part_of Y
+. exists Z :surface
+. X has_boundary Z
+. Z boundary_of Y
+
+has_boundary: http://purl.obolibrary.org/obo/RO_0002002
+boundary_of: http://purl.obolibrary.org/obo/RO_0002000",,
+RO.0002008,coincident with,A relation that holds between two linear structures that are approximately parallel to each other for their entire length and where either the two structures are adjacent to each other or one is part of the other.,,
+RO.0002009,cell expresses,"A relation that applies between a cell(c) and a gene(g) , where the process of 'transcription, DNA templated (GO_0006351)' is occuring in in cell c and that process has input gene g.",,
+RO.0002011,regulates transport of,A relationship that holds between a  process that regulates a transport process and the entity transported by that process.,,
+RO.0002012,occurrent part of,A part of relation that applies only between occurrents.,,
+RO.0002017,has component activity,,,
+RO.0002018,has component process,"w 'has process component' p if p and w are processes,  w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type.",,
+RO.0002019,has ligand,"A relationship that holds between between a receptor and an chemical entity, typically a small molecule or peptide, that carries information between cells or compartments of a cell and which binds the receptor and regulates its effector function.",,
+RO.0002020,transports,Holds between p and c when p is a transport process or transporter activity and the outcome of this p is to move c from one location to another.,,
+RO.0002021,occurs across,"A relationship between a process and a barrier, where the process occurs in a region spanning the barrier. For cellular processes the barrier is typically a membrane. Examples include transport across a membrane and membrane depolarization.",,
+RO.0002022,directly regulated by,,RO.0002578,
+RO.0002025,has effector activity,"A 'has effector activity' B if A and B are GO molecular functions (GO_0003674),  A 'has component activity' B and B is the effector (output function) of B.  Each compound function has only one effector activity.",,
+RO.0002084,during which ends,,RO.0002093,
+RO.0002085,encompasses,,RO.0002092,
+RO.0002086,ends after,,,
+RO.0002087,immediately preceded by,,RO.0002090,
+RO.0002088,during which starts,,RO.0002091,
+RO.0002089,starts before,,,
+RO.0002090,immediately precedes,,,
+RO.0002091,starts during,,,
+RO.0002092,happens during,,,
+RO.0002093,ends during,,,
+RO.0002100,has soma location,Relation between a neuron and a material anatomical entity that its soma is part of.,,
+RO.0002101,fasciculates with,"relationship between a neuron and a neuron projection bundle (e.g.- tract or nerve bundle) that one or more of its projections travels through.
+",,
+RO.0002103,synapsed by,Relation between an anatomical structure (including cells) and a neuron that chemically synapses to it. ,RO.0002120,
+RO.0002111,releases neurotransmitter,,,
+RO.0002120,synapsed to," Relation between a neuron and an anatomical structure (including cells) that it chemically synapses to.
+        ",,
+RO.0002121,dendrite synapsed in,"Relation between a neuron and some structure  (e.g.- a brain region) in which its dendrite receives synaptic input.
+
+	",,
+RO.0002131,overlaps,x overlaps y if and only if there exists some z such that x has part z and z part of y,,
+RO.0002134,innervates,"Relation between a 'neuron projection bundle' and a region in which one or more of its component neuron projections either synapses to targets or receives synaptic input.
+T innervates some R
+Expands_to: T has_fasciculating_neuron_projection that synapse_in some R.",,
+RO.0002151,partially overlaps,"x partially overlaps y iff there exists some z such that z is part of x and z is part of y, and it is also the case that neither x is part of y or y is part of x",,
+RO.0002158,shares ancestor with,two individual entities d1 and d2 stand in a shares_ancestor_with relation if and only if there exists some a such that d1 derived_by_descent_from a and d2 derived_by_descent_from a.,,
+RO.0002163,spatially disjoint from,A is spatially_disjoint_from B if and only if they have no parts in common,,
+RO.0002170,connected to,"a is connected to b if and only if a and b are discrete structure, and there exists some connecting structure c, such that c connects a and b",,
+RO.0002176,connects,"c connects a if and only if there exist some b such that a and b are similar parts of the same system, and c connects b, specifically, c connects a with b. When one structure connects two others it unites some aspect of the function or role they play within the system.",,
+RO.0002178,supplies,"Relation between an arterial structure and another structure, where the arterial structure acts as a conduit channeling fluid, substance or energy.",,
+RO.0002179,drains,"Relation between an collecting structure and another structure, where the collecting structure acts as a conduit channeling fluid, substance or energy away from the other structure.",,
+RO.0002180,has component,"w 'has component' p if w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type.",,
+RO.0002200,has phenotype,"A relationship that holds between a biological entity and a phenotype. Here a phenotype is construed broadly as any kind of quality of an organism part, a collection of these qualities, or a change in quality or qualities (e.g. abnormally increased temperature). The subject of this relationship can be an organism (where the organism has the phenotype, i.e. the qualities inhere in parts of this organism), a genomic entity such as a gene or genotype (if modifications of the gene or the genotype causes the phenotype), or a condition such as a disease (such that if the condition inheres in an organism, then the organism has the phenotype).",,
+RO.0002202,develops from,x develops from y if and only if either (a) x directly develops from y or (b) there exists some z such that x directly develops from z and z develops from y,RO.0002203,
+RO.0002203,develops into,inverse of develops from,,
+RO.0002205,has gene product,x has gene product y if and only if x is a gene (SO:0000704) that participates in some gene expression process (GO:0010467) where the output of that process is either y or something that is ribosomally translated from y,,
+RO.0002206,expressed in,"x expressed in y if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and the transcription of x is part of the gene expression process (iii) x is a mature gene product such as a protein, and x was translated or otherwise processes from a transcript that was transcribed as part of this gene expression process",RO.0002292,
+RO.0002207,directly develops from,"Candidate definition: x directly_develops from y if and only if there exists some developmental process (GO:0032502) p such that x and y both participate in p, and x is the output of p and y is the input of p, and a substantial portion of the matter of x comes from y, and the start of x is coincident with or after the end of y.",RO.0002210,
+RO.0002209,has parasitoid,inverse of parasitoid of,,
+RO.0002210,directly develops into,inverse of directly develops from,,
+RO.0002211,regulates,"p regulates q iff p is causally upstream of q, the execution of p is not constant and varies according to specific conditions, and p influences the rate or magnitude of execution of q due to an effect either on some enabler of q or some enabler of a part of q.",RO.0002334,
+RO.0002212,negatively regulates,"p negatively regulates q iff p regulates q, and p decreases the rate or magnitude of execution of q.",RO.0002335,
+RO.0002213,positively regulates,"p positively regulates q iff p regulates q, and p increases the rate or magnitude of execution of q.",RO.0002336,
+RO.0002214,has prototype,"x has prototype y if and only if x is an instance of C and y is a prototypical instance of C. For example, every instance of heart, both normal and abnormal is related by the has prototype relation to some instance of a ""canonical"" heart, which participates in blood circulation.",,
+RO.0002219,surrounded by,"x surrounded_by y if and only if (1) x is adjacent to y and for every region r that is adjacent to x, r overlaps y (2) the shared boundary between x and y occupies the majority of the outermost boundary of x",RO.0002221,
+RO.0002221,surrounds,inverse of surrounded by,,
+RO.0002222,temporally related to,,,
+RO.0002223,starts,inverse of starts with,RO.0002224,
+RO.0002224,starts with,"x starts with y if and only if x has part y and the time point at which x starts is equivalent to the time point at which y starts. Formally: α(y) = α(x) ∧ ω(y) < ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,
+RO.0002226,develops in,x develops_in y if x is located in y whilst x is developing,,
+RO.0002227,obligate parasite of,A sub-relation of parasite-of in which the parasite that cannot complete its life cycle without a host.,,
+RO.0002229,ends,inverse of ends with,RO.0002230,
+RO.0002230,ends with,"x ends with y if and only if x has part y and the time point at which x ends is equivalent to the time point at which y ends. Formally: α(y) > α(x) ∧ ω(y) = ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,
+RO.0002231,has start location,x 'has starts location' y if and only if there exists some process z such that x 'starts with' z and z 'occurs in' y,,
+RO.0002232,has end location,x 'has end location' y if and only if there exists some process z such that x 'ends with' z and z 'occurs in' y,,
+RO.0002233,has input,"p has input c iff: p is a process, c is a material entity, c is a participant in p, c is present at the start of p, and the state of c is modified during p.",,
+RO.0002234,has output,"p has output c iff c is a participant in p, c is present at the end of p, and c is not present in the same state at the beginning of p.",,
+RO.0002238,has component participant,X 'has component participant' Y means X 'has participant' Y and there is a cardinality constraint that specifies the numbers of Ys.,,
+RO.0002240,has exposure receptor,"A broad relationship between an exposure event or process and any entity (e.g., an organism, organism population, or an organism part) that interacts with an exposure stimulus during the exposure event.",,
+RO.0002241,has exposure stressor,"A broad relationship between an exposure event or process and any agent, stimulus, activity, or event that causes stress or tension on an organism and interacts with an exposure receptor during an exposure event.",,
+RO.0002242,has exposure route,A broad relationship between an exposure event or process and a process by which the exposure stressor comes into contact with the exposure receptor,,
+RO.0002245,over-expressed in,"g is over-expressed in t iff g is expressed in t, and the expression level of g is increased relative to some background.",,
+RO.0002246,under-expressed in,"g is under-expressed in t iff g is expressed in t, and the expression level of g is decreased relative to some background.",,
+RO.0002248,has active ingredient,"A relationship that holds between a substance and a chemical entity, if the chemical entity is part of the substance, and the chemical entity forms the biologically active component of the substance.",,
+RO.0002252,connecting branch of,"b connecting-branch-of s iff b is connected to s, and there exists some tree-like structure t such that the mereological sum of b plus s is either the same as t or a branching-part-of t.",RO.0002253,
+RO.0002253,has connecting branch,inverse of connecting branch of,,
+RO.0002255,developmentally contributes to,inverse of has developmental contribution from,,
+RO.0002256,developmentally induced by,"t1 induced_by t2 if there is a process of developmental induction (GO:0031128) with t1 and t2 as interacting participants. t2 causes t1 to change its fate from a precursor material anatomical entity type T to T', where T' develops_from T",RO.0002257,
+RO.0002257,developmentally induces,Inverse of developmentally induced by,,
+RO.0002258,developmentally preceded by,"Candidate definition: x developmentally related to y if and only if there exists some developmental process (GO:0032502) p such that x and y both participates in p, and x is the output of p and y is the input of p",RO.0002286,
+RO.0002260,has biological role,c has-biological-role r iff c has-role r and r is a biological role (CHEBI:24432),,
+RO.0002261,has application role,c has-application-role r iff c has-role r and r is an application role (CHEBI:33232),,
+RO.0002262,has chemical role,c has-chemical-role r iff c has-role r and r is a chemical role (CHEBI:51086),,
+RO.0002263,acts upstream of,"c acts upstream of p if and only if c enables some f that is involved in p' and p' occurs chronologically before p, is not part of p, and affects the execution of p. c is a material entity and f, p, p' are processes.",,
+RO.0002286,developmentally succeeded by,Inverse of developmentally preceded by,,
+RO.0002291,ubiquitously expressed in,"x is ubiquitously expressed in y if and only if x is expressed in y, and the majority of cells in y express x",RO.0002293,
+RO.0002292,expresses,"y expresses x if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and x was transcribed from a gene as part of the gene expression process (iii) x is a mature gene product (protein or RNA), and x was translated or otherwise processed from a transcript that was transcribed as part of the gene expression process.",,
+RO.0002293,ubiquitously expresses,inverse of ubiquiotously expressed in,,
+RO.0002303,has habitat,"x 'has habitat' y if and only if: x is an organism, y is a habitat, and y can sustain and allow the growth of a population of xs.",,
+RO.0002309,has exposure stimulus,"A relationship between an exposure event or process and any agent, stimulus, activity, or event that causally effects an organism and interacts with an exposure receptor during an exposure event.",,
+RO.0002320,evolutionarily related to,A relationship that holds via some environmental process,,
+RO.0002321,ecologically related to,A relationship that is mediated in some way by the environment or environmental feature (ENVO:00002297),,
+RO.0002322,confers advantage in,,,
+RO.0002323,mereotopologically related to,A mereological relationship or a topological relationship,,
+RO.0002324,developmentally related to,A relationship that holds between entities participating in some developmental process (GO:0032502),,
+RO.0002325,colocalizes with,a colocalizes_with b if and only if  a is transiently or peripherally associated with b[GO].,,
+RO.0002326,contributes to,,,
+RO.0002327,enables,c enables p iff c is capable of p and c acts to execute p.,RO.0002333,
+RO.0002328,functionally related to,"A grouping relationship for any relationship directly involving a function, or that holds because of a function of one of the related entities.",,
+RO.0002330,genomically related to,"holds between two entities when some genome-level process such as gene expression is involved. This includes transcriptional, spliceosomal events. These relations can be used between either macromolecule entities (such as regions of nucleic acid) or between their abstract informational counterparts.",,
+RO.0002331,involved in,"c involved_in p if and only if c enables some process p', and p' is part of p",,
+RO.0002332,regulates levels of,p regulates levels of c if p regulates some amount (PATO:0000070) of c,,
+RO.0002333,enabled by,inverse of enables,,
+RO.0002334,regulated by,inverse of regulates,,
+RO.0002335,negatively regulated by,inverse of negatively regulates,,
+RO.0002336,positively regulated by,inverse of positively regulates,,
+RO.0002340,imports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the start location of c is part of the region that surrounds s.",,
+RO.0002345,exports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the end location of c is part of the region that surrounds s.",,
+RO.0002351,has member,has member is a mereological relation between a collection and an item.,,
+RO.0002360,has dendrite location,,,
+RO.0002371,attached to,"a is attached to b if and only if a and b are discrete objects or object parts, and there are physical connections between a and b such that a force pulling a will move b, or a force pulling b will move a",,
+RO.0002372,has muscle origin,"m has_muscle_origin s iff m is attached_to s, and it is the case that when m contracts, s does not move. The site of the origin tends to be more proximal and have greater mass than what the other end attaches to.",,
+RO.0002373,has muscle insertion,"m has_muscle_insertion s iff m is attaches_to s, and it is the case that when m contracts, s moves. Insertions are usually connections of muscle via tendon to bone.",,
+RO.0002380,branching part of,x is a branching part of y if and only if x is part of y and x is connected directly or indirectly to the main stem of y,RO.0002569,
+RO.0002400,has direct input,"p has direct input c iff c is a participant in p, c is present at the start of p, and the state of c is modified during p.",,
+RO.0002407,indirectly positively regulates,p indirectly positively regulates q iff p is indirectly causally upstream of q and p positively regulates q.,,
+RO.0002409,indirectly negatively regulates,p indirectly negatively regulates q iff p is indirectly causally upstream of q and p negatively regulates q.,,
+RO.0002410,causally related to,"relation that links two events, processes, states, or objects such that one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly or wholly responsible for the effect, and the effect is partly or wholly dependent on the cause.",,
+RO.0002413,provides input for,"p provides input for q iff p is immediately causally upstream of q, and there exists some c such that p has_output c and q has_input c.",,
+RO.0002424,differs in,,,
+RO.0002432,is active in,"c executes activity in d if and only if c enables p and p occurs_in d.  Assuming no action at a distance by gene products, if a gene product enables (is capable of) a process that occurs in some structure, it must have at least some part in that structure.",,
+RO.0002434,interacts with,A relationship that holds between two entities in which the processes executed by the two entities are causally connected.,,
+RO.0002435,genetically interacts with,,,
+RO.0002436,molecularly interacts with,An interaction relationship in which the two partners are molecular entities that directly physically interact with each other for example via a stable binding interaction or a brief interaction during which one modifies the other.,,
+RO.0002437,biotically interacts with,An interaction relationship in which at least one of the partners is an organism and the other is either an organism or an abiotic entity with which the organism interacts.,,
+RO.0002438,trophically interacts with,An interaction relationship in which the partners are related via a feeding relationship.,,
+RO.0002439,preys on,"An interaction relationship involving a predation process, where the subject kills the target in order to eat it or to feed to siblings, offspring or group members",RO.0002458,
+RO.0002440,symbiotically interacts with,A biotic interaction in which the two organisms live together in more or less intimate association.,,
+RO.0002441,commensually interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which one benefits and the other is unaffected (GO).,,
+RO.0002442,mutualistically interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which both organisms benefit from each other (GO).,,
+RO.0002445,parasitized by,Inverse of parasite of,,
+RO.0002451,transmitted by,A relationship that holds between a disease and organism,,
+RO.0002452,has symptom,A relation that holds between a disease or an organism and a phenotype,,
+RO.0002454,has host,"X 'has host' y if and only if: x is an organism, y is an organism, and x can live on the surface of or within the body of y",,
+RO.0002455,pollinates,,RO.0002456,
+RO.0002456,pollinated by,,,
+RO.0002457,acquires nutrients from,Inverse of provides nutrients for,RO.0002469,
+RO.0002458,preyed upon by,inverse of preys on,,
+RO.0002459,is vector for,,RO.0002460,
+RO.0002460,has vector,,,
+RO.0002465,is symbiosis,,,
+RO.0002466,is commensalism,,,
+RO.0002467,is mutualism,,,
+RO.0002468,is parasitism,,,
+RO.0002469,provides nutrients for,A biotic interaction where a material entity provides nutrition for an organism.,,
+RO.0002470,eats,A biotic interaction where one organism consumes a material entity through a type of mouth or other oral opening.,,
+RO.0002471,is eaten by,Inverse of eats,,
+RO.0002472,is evidence for,"A relationship between a piece of evidence a and some entity b, where b is  an information content entity, material entity or process, and 
+the a supports either the existence of b, or the truth value of b.",RO.0002558,
+RO.0002473,composed primarily of,x composed_primarily_of y if and only if more than half of the mass of x is made from y or units of the same type as y.,,
+RO.0002480,ubiquitinates,An interaction relation between x and y in which x catalyzes a reaction in which one or more ubiquitin groups are added to y,,
+RO.0002481,is kinase activity,,,
+RO.0002482,is ubiquitination,,,
+RO.0002485,receives input from,,RO.0002486,
+RO.0002486,sends output to,,,
+RO.0002488,existence starts during,x existence starts during y if and only if the time point at which x starts is after or equivalent to the time point at which y starts and before or equivalent to the time point at which y ends. Formally: x existence starts during y iff α(x) >= α(y) & α(x) <= ω(y).,,
+RO.0002489,existence starts with,x starts ends with y if and only if the time point at which x starts is equivalent to the time point at which y starts. Formally: x existence starts with y iff α(x) = α(y).,,
+RO.0002490,existence overlaps,x existence overlaps y if and only if either (a) the start of x is part of y or (b) the end of x is part of y. Formally: x existence starts and ends during y iff (α(x) >= α(y) & α(x) <= ω(y)) OR (ω(x) <= ω(y) & ω(x) >= α(y)),,
+RO.0002492,existence ends during,x existence ends during y if and only if the time point at which x ends is before or equivalent to the time point at which y ends and after or equivalent to the point at which y starts. Formally: x existence ends during y iff ω(x) <= ω(y) and ω(x) >= α(y).,,
+RO.0002493,existence ends with,x existence ends with y if and only if the time point at which x ends is equivalent to the time point at which y ends. Formally: x existence ends with y iff ω(x) = ω(y).,,
+RO.0002502,depends on,,,
+RO.0002505,has intermediate,"p has intermediate c if and only if p has parts p1, p2 and p1 has output c, and p2 has input c",,
+RO.0002507,determined by,"s determined by f if and only if s is a type of system, and f is a material entity that is part of s, such that f exerts a strong causal influence on the functioning of s, and the removal of f would cause the collapse of s.",RO.0002508,
+RO.0002508,determines,inverse of determined by,,
+RO.0002510,transcribed from,x is transcribed from y if and only if x is synthesized from template y,RO.0002511,
+RO.0002511,transcribed to,inverse of transcribed from,,
+RO.0002513,ribosomally translates to,inverse of ribosomal translation of,,
+RO.0002514,sequentially related to,A relation that holds between two entities that have the property of being sequences or having sequences. ,,
+RO.0002516,has start sequence,"x has start sequence y if the start of x is identical to the start of y, and x has y as a subsequence",,
+RO.0002518,has end sequence,"x has end sequence y if the end of x is identical to the end of y, and x has y as a subsequence",,
+RO.0002522,bounds sequence of,"x bounds the sequence of y iff the upstream-most part of x is upstream of or coincident with the upstream-most part of y, and the downstream-most part of x is downstream of or coincident with the downstream-most part of y",,
+RO.0002524,has subsequence,x has subsequence y iff all of the sequence parts of y are sequence parts of x,RO.0002525,
+RO.0002525,is subsequence of,inverse of has subsequence,,
+RO.0002551,has skeleton,A relation between a segment or subdivision of an organism and the maximal subdivision of material entities that provides structural support for that segment or subdivision.,,
+RO.0002554,hyperparasitized by,inverse of hyperparasite of,,
+RO.0002557,has pathogen,A host interaction where the smaller of the two members of a symbiosis causes a disease in the larger member,,
+RO.0002558,has evidence,inverse of is evidence for,,
+RO.0002559,causally influenced by,,RO.0002566,
+RO.0002566,causally influences,"The entity or characteristic A is causally upstream of the entity or characteristic B, A having an effect on B. An entity corresponds to any biological type of entity as long as a mass is measurable. A characteristic corresponds to a particular specificity of an entity (e.g., phenotype, shape, size).",,
+RO.0002567,biomechanically related to,A relation that holds between elements of a musculoskeletal system or its analogs.,,
+RO.0002568,has muscle antagonist,"m1 has_muscle_antagonist m2 iff m1 has_muscle_insertion s, m2 has_muscle_insection s, m1 acts in opposition to m2, and m2 is responsible for returning the structure to its initial position.",,
+RO.0002569,has branching part,inverse of branching part of,,
+RO.0002573,has modifier,A relation that holds between an attribute or a qualifier and another attribute.,,
+RO.0002578,directly regulates,p directly regulates q iff p is immediately causally upstream of q and p regulates q.,,
+RO.0002596,capable of regulating,"Holds between c and p if and only if c is capable of some activity a, and a regulates p.",,
+RO.0002607,is marker for,"c is marker for d iff the presence or occurrence of d is correlated with the presence of occurrence of c, and the observation of c is used to infer the presence or occurrence of d. Note that this does not imply that c and d are in a direct causal relationship, as it may be the case that there is a third entity e that stands in a direct causal relationship with c and d.",,
+RO.0002610,correlated with,"A relationship that holds between two entities, where the entities exhibit a statistical dependence relationship. The entities may be statistical variables, or they may be other kinds of entities such as diseases, chemical entities or processes.",,
+RO.0002615,has model,Inverse of is-model-of,,
+RO.0002618,visits,,RO.0002619,
+RO.0002619,visited by,,,
+RO.0002622,visits flowers of,,,
+RO.0002624,lays eggs in,,,
+RO.0002626,kills,,RO.0002627,
+RO.0002627,is killed by,,,
+RO.0002629,directly positively regulates,"p directly positively regulates q iff p is immediately causally upstream of q, and p positively regulates q.",,
+RO.0002630,directly negatively regulates,"p directly negatively regulates q iff p is immediately causally upstream of q, and p negatively regulates q.",,
+RO.0002633,has ectoparasite,inverse of ectoparasite of,,
+RO.0002635,has endoparasite,,,
+RO.0002637,has mesoparasite,inverse of mesoparasite of,,
+RO.0002639,has intercellular endoparasite,inverse of intercellular endoparasite of,,
+RO.0002641,has intracellular endoparasite,inverse of intracellular endoparasite of,,
+RO.0002801,co-roosts with,"Two or more individuals sharing the same roost site (cave, mine, tree or tree hollow, animal burrow, leaf tent, rock crack, space in man-made structure, etc.). Individuals that are sharing a communal roost may be said to be co-roosting. The roost may be either a day roost where the individuals rest during daytime hours, or a night roost where individuals roost to feed, groom, or rest in between flights and/or foraging bouts.  Communal roosting as thus defined is an umbrella term within which different specialized types -- which are not mutually exclusive -- may be recognized based on taxonomy and the temporal and spatial relationships of the individuals that are co-roosting.",,
+RO.0002803,has reservoir host,inverse of reservoir host of,,
+RO.0003001,produced by,a produced_by b iff some process that occurs_in b has_output a.,,
+RO.0003002,represses expression of,Holds between entity A (a transcription factor) and a nucleic acid B if and only if A down-regulates the expression of B. The nucleic acid can be a gene or an mRNA.,,
+RO.0003003,increases expression of,Holds between entity A (a transcription factor) and nucleic acid B if and only if A up-regulates the expression of B. The nucleic acid can be a gene or mRNA.,,
+RO.0003303,causes condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some causal role for the condition.",,
+RO.0003304,contributes to condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some contributing role that influences the condition.",,
+RO.0003307,ameliorates condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the presence of the entity reduces or eliminates some or all aspects of the condition.",,
+RO.0003308,correlated with condition,A relationship between an entity and a condition (phenotype or disease) with which it exhibits a statistical dependence relationship.,,
+RO.0003309,exacerbates condition,"A relationship between an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) and a condition (a phenotype or disease), where the presence of the entity worsens some or all aspects of the condition.",,
+RO.0003310,condition ameliorated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are reduced or eliminated by the presence of the entity.",,
+RO.0003311,condition exacerbated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are worsened by the presence of the entity.",,
+RO.0004008,has primary output,"p has primary output c if (a) p has output c and (b) the goal of process is to modify, produce, or transform c.",,
+RO.0004009,has primary input,"p has primary input c if (a) p has input c and (b) the goal of process is to modify, consume, or transform c.",,
+RO.0004026,disease has location,A relationship between a disease and an anatomical entity where the disease has one or more features that are located in that entity.,,
+RO.0004029,disease has feature,"A relationship between a disease and some feature of that disease, where the feature is either a phenotype or an isolated disease.",,
+RO.0004031,enables subfunction,"Holds between an entity and an process P where the entity enables some larger compound process, and that larger process has-part P.",,
+RO.0007000,has driver,"A relation between two entities, in which one of the entities is any natural or human-influenced factor that directly or indirectly causes a change in the other entity.",,
+RO.0007001,has disease driver,"A relation between an entity and a disease of a host, in which the entity is not part of the host itself, and the condition results in pathological processes.",,
+RO.0008502,has epiphyte,inverse of epiphyte of,,
+RO.0008504,kleptoparasitized by,inverse of kleptoparasite of,,
+RO.0008505,creates habitat for,An interaction relationship wherein one organism creates a structure or environment that is lived in by another organism.,,
+RO.0008506,ecologically co-occurs with,An interaction relationship describing organisms that often occur together at the same time and space or in the same environment.,,
+RO.0008507,lays eggs on,An interaction relationship in which organism a lays eggs on the outside surface of organism b. Organism b is neither helped nor harmed in the process of egg laying or incubation.,,
+RO.0008509,has roost,"x 'has roost' y if and only if: x is an organism, y is a habitat, and y can support rest behaviors x.",,
+RO.0009001,has substance added,"""has substance added"" is a relation existing between a (physical) entity and a substance in which the entity has had the substance added to it at some point in time.",,
+RO.0009002,has substance removed,"""has substance removed"" is a relation existing between two physical entities in which the first entity has had the second entity (a substance) removed from it at some point in time.",,
+RO.0009003,immersed in,"""immersed in"" is a relation between a (physical) entity and a fluid substance in which the entity is wholely or substantially surrounded by the substance.",,
+RO.0009004,has consumer,'has consumer' is a relation between a material entity and an organism in which the former can normally be digested or otherwise absorbed by the latter without immediate or persistent ill effect.,,
+RO.0009006,assay measures characteristic,"A relation between an assay and a characteristic, in which the assay generates a data item which is a measure of a characteristic.",,
+RO.0010001,generically depends on,A generically dependent continuant *b* generically depends on an independent continuant *c* at time *t* means: there inheres in *c* a specifically deendent continuant which concretizes *b* at *t*.,RO.0010002,
+RO.0010002,is carrier of,*b* is carrier of *c* at time *t* if and only if *c* *g-depends on* *b* at *t*,,
+RO.0011002,regulates activity of,"The entity A has an activity that regulates an activity of the entity B. For example, A and B are gene products where the catalytic activity of A regulates the kinase activity of B.",,
+RO.0011003,regulates quantity of,The entity A has an activity that regulates the quantity or abundance or concentration of the entity B.,,
+RO.0011014,destabilizes quantity of,An entity A directly interacts with B and A has an activity that decreases the amount of an entity B by degradating it.,,
+RO.0011015,stabilizes quantity of,An entity A physically interacts with B and A has an activity that increases the amount of an entity B by stabilizing it.,,
+RO.0012010,removes input for,"p removes input for q iff p is causally upstream of q, there exists some c such that p has_input c and q has_input c, p reduces the levels of c, and c is rate limiting for execution of q.",,
+RO.0012012,indirectly regulates,p indirectly regulates q iff p is indirectly causally upstream of q and p regulates q.,,
+RO.0015001,has exemplar data,A relation between a material entity and some data in which the data is taken as exemplifying the material entity.,,
+RO.0015010,has relative magnitude,,,
+RO.0015011,has cross section,"s3 has_cross_section s3 if and only if : there exists some 2d plane that intersects the bearer of s3, and the impression of s3 upon that plane has shape quality s2.",,
+RO.0016002,has disease,A relationship that holds between an organism and a disease. Here a disease is construed broadly as a disposition to undergo pathological processes that exists in an organism because of one or more disorders in that organism.,,
+RO.0016004,has exposure medium,"X has exposure medium Y if X is an exposure event (process), Y is a material entity, and the stimulus for X is transmitted or carried in Y.",,
+RO.0017001,device utilizes material,"X device utilizes material Y means X and Y are material entities, and X is capable of some process P that has input Y.",,
+RO.0017003,positively correlated with,A relation between entities in which one increases or decreases as the other does the same.,,
+RO.0017004,negatively correlated with,A relation between entities in which one increases as the other decreases.,,
+RO.0017005,contains measured amount,A relation between a container and measurement datum that specifies the actual amount of material in the container.,,
+RO.0017006,has maximum capacity,A relation that relates a container to a measurement datum that specifies the maximum capacity of the container. Capacity can refer to either weight or volume.,,
+RO.0017008,owns,A primitive relation that holds between entities x and y in which y is at x's full disposal.,RO.0017009,
+RO.0017009,is owned by,Inverse of the owns relation.,,
+RO.0018001,is myristoyltransferase activity,Helper relation for OWL definition of RO:0018002 myristoylates,,
+RO.0018003,myristoylated by,inverse of myristoylates,,
+RO.0018027,is agonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor activates some activity of the receptor,,
+RO.0018029,is antagonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor reduces some activity of the receptor to basal level,,
+RO.0018036,is tautomer of,"Two chemicals are tautomers if they can be readily interconverted.
+
+This commonly refers to prototropy in which a hydrogen's position is changed, such as between ketones and enols. This is also often observed in heterocyclic rings, e.g., ones containing nitrogens and/or have aryl functional groups containing heteroatoms.",,
+RO.0018038,has functional parent,"Chemical A has functional parent Chemical B if there is chemical transformation through which chemical B can be produced from chemical A. 
+
+For example, the relationship between a salt and a freebased compound is a ""has functional parent"" relationship.",,
+RO.0018039,is enantiomer of,"Chemicals A and B are enantiomers if they share the same molecular graph except the change of the configuration of substituents around exactly one chiral center.
+
+A chemical with no chiral centers can not have an enantiomer. A chemical with multiple chiral centers can have multiple enantiomers, but its enantiomers are not themselves enantiomers (they are diastereomers).",,
+RO.0018040,has parent hydride,Chemical A has parent hydride Chemical B if there exists a molecular graphical transformation where functional groups on A are replaced with hydrogens in order to yield B.,,
+RO.0019000,regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) regulates characteristic (C) iff: P results in the existence of C OR affects the intensity or magnitude of C.,,
+RO.0019001,positively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) positively regulates characteristic (C) iff: P results in an increase in the intensity or magnitude of C.,,
+RO.0019002,negatively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) negatively regulates characteristic (C) iff: P results in a decrease in the intensity or magnitude of C.,,
+RO.0020202,has numerator,A relationship between a ratio or proportion and its dividend.,,
+RO.0020203,has denominator,A relationship between a ratio or proportion and its divisor.,,
+PEL.000020,interrupts,stop the continuous progress of (an activity or process),,
+PEL.000034,stimulates,excite to activity or growth or to greater activity transactivate,,
+PEL.000038,activated by,converse of 'activates': X is activated by Y when Y activates X,,
+PEL.000039,actuated by,converse of 'actuates': X is actuated by Y when Y actuates X,,
+PEL.000040,associated with,converse of 'associates': X is associated with Y when Y associates X,,
+PEL.000041,bound by,converse of 'binds to': X is bound by Y when Y binds to X,,
+PEL.000042,caused by,converse of 'causes': X is caused by Y when Y causes X,,
+PEL.000043,constrained by,converse of 'constrains': X is constrained by Y when Y constrains X,,
+PEL.000044,controlled by,converse of 'controls': X is controlled by Y when Y controls X,,
+PEL.000045,degraded by,converse of 'degrades': X is degraded by Y when Y degrades X,,
+PEL.000046,destroyed by,converse of 'destroys': X is destroyed by Y when Y destroys X,,
+PEL.000047,directed by,converse of 'directs': X is directed by Y when Y directs X,,
+PEL.000048,downregulated by,converse of 'downregulates': X is downregulated by Y when Y downregulates X,,
+PEL.000049,enhanced by,converse of 'enhances': X is enhanced by Y when Y enhances X,,
+PEL.000050,facilitated by,converse of 'facilitates': X is facilitated by Y when Y facilitates X,,
+PEL.000051,forced by,converse of 'forces': X is forced by Y when Y forces X,,
+PEL.000052,hindered by,converse of 'hinders': X is hindered by Y when Y hinders X,,
+PEL.000053,inactivated by,converse of 'inactivates': X is inactivated by Y when Y inactivates X,,
+PEL.000054,increased by,converse of 'increases': X is increased by Y when Y increases X,,
+PEL.000055,induced by,converse of 'induces': X is induced by Y when Y induces X,,
+PEL.000056,inhibited by,converse of 'inhibits': X is inhibited by Y when Y inhibits X,,
+PEL.000057,initiated by,converse of 'initiates': X is initiated by Y when Y initiates X,,
+PEL.000058,interrupted by,converse of 'interrupts': X is interrupted by Y when Y interrupts X,,
+PEL.000059,limited by,converse of 'limits': X is limited by Y when Y limits X,,
+PEL.000060,mentioned by,converse of 'mentions': X is mentioned by Y when Y mentions X,,
+PEL.000061,overproduced by,converse of 'overproduces': X is overproduced by Y when Y overproduces X,,
+PEL.000062,phosphorylated by,converse of 'phosphorylates': X is phosphorylated by Y when Y phosphorylates X,,
+PEL.000063,promoted by,converse of 'promotes': X is promoted by Y when Y promotes X,,
+PEL.000064,provoked by,converse of 'provokes': X is provoked by Y when Y provokes X,,
+PEL.000065,reduced by,converse of 'reduces': X is reduced by Y when Y reduces X,,
+PEL.000066,repressed by,converse of 'represses': X is repressed by Y when Y represses X,,
+PEL.000067,reverted by,converse of 'reverts': X is reverted by Y when Y reverts X,,
+PEL.000068,secreted by,converse of 'secretes': X is secreted by Y when Y secretes X,,
+PEL.000069,stimulated by,converse of 'stimulates': X is stimulated by Y when Y stimulates X,,
+PEL.000070,suppressed by,converse of 'suppresses': X is suppressed by Y when Y suppresses X,,
+PEL.000071,triggered by,converse of 'triggers': X is triggered by Y when Y triggers X,,
+PEL.000072,upregulated by,converse of 'upregulates': X is upregulated by Y when Y upregulates X,,
diff --git a/data/derived/properties.synthesis.2.pairs.csv b/data/derived/properties.synthesis.2.pairs.csv
new file mode 100644
index 0000000..9ca0d77
--- /dev/null
+++ b/data/derived/properties.synthesis.2.pairs.csv
@@ -0,0 +1,377 @@
+entity_id,label,description,inverse_entity_id,inverse_label,inverse_source,needs_review,is_symmetric,is_canonical,canonical_entity_id,example
+BFO.0000051,has part,a core relation that holds between a whole and its part,,,,False,False,True,BFO.0000051,
+BFO.0000054,realized in,,BFO.0000055,realizes,ro,False,False,False,BFO.0000055,
+BFO.0000055,realizes,,BFO.0000054,realized in,passive_prep,False,False,True,BFO.0000055,
+BFO.0000062,preceded by,"x is preceded by y if and only if the time point at which y ends is before or equivalent to the time point at which x starts. Formally: x preceded by y iff ω(y) <= α(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",BFO.0000063,precedes,ro,False,False,False,BFO.0000063,
+BFO.0000063,precedes,"x precedes y if and only if the time point at which x ends is before or equivalent to the time point at which y starts. Formally: x precedes y iff ω(x) <= α(y), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",BFO.0000062,preceded by,passive_by,False,False,True,BFO.0000063,
+BFO.0000066,occurs in,b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t,BFO.0000067,contains process,ro,False,False,True,BFO.0000066,
+BFO.0000067,contains process,[copied from inverse property 'occurs in'] b occurs_in c =def b is a process and c is a material entity or immaterial entity& there exists a spatiotemporal region r and b occupies_spatiotemporal_region r.& forall(t) if b exists_at t then c exists_at t & there exist spatial regions s and s’ where & b spatially_projects_onto s at t& c is occupies_spatial_region s’ at t& s is a proper_continuant_part_of s’ at t,,,,False,False,True,BFO.0000067,
+PEL.000000,activates,"convert (something, such as a provitamin) into a biologically active derivative",PEL.000038,activated by,passive_by,False,False,True,PEL.000000,
+PEL.000001,actuates,cause to act in a particular way; motivate,PEL.000039,actuated by,passive_by,False,False,True,PEL.000001,
+PEL.000002,associates,combine or join with other parts,,,,False,True,True,PEL.000002,
+PEL.000003,binds to,"to combine with, form a bond with, or be taken up by a chemical or chemical structure",,,,False,True,True,PEL.000003," ""An enzyme is structured in such a way as to be able to bind with its substrate"""
+PEL.000004,causes,make happen,PEL.000042,caused by,passive_by,False,False,True,PEL.000004,
+PEL.000005,constrains,"severely restrict the scope, extent, or activity of",PEL.000043,constrained by,passive_by,False,False,True,PEL.000005,
+PEL.000006,controls,determine the behaviour or supervise the running of,PEL.000044,controlled by,passive_by,False,False,True,PEL.000006,
+PEL.000007,degrades,break down or deteriorate chemically,PEL.000045,degraded by,passive_by,False,False,True,PEL.000007,
+PEL.000008,destroys,end the existence of something by damaging or attacking it,PEL.000046,destroyed by,passive_by,False,False,True,PEL.000008,
+PEL.000009,directs,control the operations of; manage or govern,PEL.000047,directed by,passive_by,False,False,True,PEL.000009,
+PEL.000010,downregulates,"lower the rate or level of (a process such as gene expression, or sensitivity to a physiologically active substance) by downregulation",PEL.000048,downregulated by,passive_by,False,False,True,PEL.000010,
+PEL.000011,enhances,"intensify, increase, or further improve the quality",PEL.000049,enhanced by,passive_by,False,False,True,PEL.000011,
+PEL.000012,facilitates,make (an action or process) easy or easier,PEL.000050,facilitated by,passive_by,False,False,True,PEL.000012,
+PEL.000013,forces,make something happen,PEL.000051,forced by,passive_by,False,False,True,PEL.000013,
+PEL.000014,hinders,make it difficult for something to happen,PEL.000052,hindered by,passive_by,False,False,True,PEL.000014,
+PEL.000015,inactivates,make inactive or inoperative,PEL.000053,inactivated by,passive_by,False,False,True,PEL.000015,
+PEL.000016,increases,"become or make greater in size, amount, or degree",PEL.000054,increased by,passive_by,False,False,True,PEL.000016,
+PEL.000017,induces,bring about or give rise to,PEL.000055,induced by,passive_by,False,False,True,PEL.000017,none of these measures induced a change of policy
+PEL.000018,inhibits,"hinder, restrain, or prevent (an action or process)",PEL.000056,inhibited by,passive_by,False,False,True,PEL.000018,
+PEL.000019,initiates,cause (a process or action) to begin,PEL.000057,initiated by,passive_by,False,False,True,PEL.000019,he proposes to initiate discussions on planning procedures
+PEL.000022,limits,curtail or reduce in quantity or extent,PEL.000059,limited by,passive_by,False,False,True,PEL.000022,
+PEL.000023,mentions,refer to something,PEL.000060,mentioned by,passive_by,False,False,True,PEL.000023,
+PEL.000024,overproduces,produce an excessive amount,PEL.000061,overproduced by,passive_by,False,False,True,PEL.000024,"if soils are too fertile, the vines can overproduce, leading to vegetal aromas and flavors"
+PEL.000025,phosphorylates,introduce a phosphate group into (a molecule or compound),PEL.000062,phosphorylated by,passive_by,False,False,True,PEL.000025,
+PEL.000026,produces,cause (a particular result or situation) to happen or exist,RO.0003001,produced by,passive_by,False,False,True,PEL.000026,
+PEL.000027,promotes,,PEL.000063,promoted by,passive_by,False,False,True,PEL.000027,
+PEL.000028,provokes,call forth an action,PEL.000064,provoked by,passive_by,False,False,True,PEL.000028,
+PEL.000029,reduces,"diminish in size, amount, extent, or number",PEL.000065,reduced by,passive_by,False,False,True,PEL.000029,
+PEL.000030,represses,inactivate (a gene or formation of a gene product),PEL.000066,repressed by,passive_by,False,False,True,PEL.000030,
+PEL.000031,reverts,return to previous state,PEL.000067,reverted by,passive_by,False,False,True,PEL.000031,
+PEL.000032,secretes,"(of a cell, gland, or organ) produce and discharge (a substance)",PEL.000068,secreted by,passive_by,False,False,True,PEL.000032,insulin is secreted in response to rising levels of glucose in the blood
+PEL.000033,shuts,cause to cease or suspend an operation or activity,,,,False,False,True,PEL.000033,
+PEL.000035,suppresses,inhibit the genetic expression of,PEL.000070,suppressed by,passive_by,False,False,True,PEL.000035,
+PEL.000036,triggers,initiate or set off,PEL.000071,triggered by,passive_by,False,False,True,PEL.000036,
+PEL.000037,upregulates,"increase the rate or level of (a process such as gene expression, or sensitivity to a physiologically active substance) by upregulation",PEL.000072,upregulated by,passive_by,False,False,True,PEL.000037,
+RO.0000053,has characteristic,Inverse of characteristic_of,,,,False,False,True,RO.0000053,
+RO.0000056,participates in,"a relation between a continuant and a process, in which the continuant is somehow involved in the process",RO.0000057,has participant,ro,False,False,True,RO.0000056,
+RO.0000057,has participant,"a relation between a process and a continuant, in which the continuant is somehow involved in the process",,,,False,False,True,RO.0000057,
+RO.0000058,is concretized as,"A relationship between a generically dependent continuant and a specifically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. A generically dependent continuant may be concretized as multiple specifically dependent continuants.",RO.0000059,concretizes,ro,False,False,True,RO.0000058,
+RO.0000059,concretizes,"A relationship between a specifically dependent continuant and a generically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. Multiple specifically dependent continuants can concretize the same generically dependent continuant.",,,,False,False,True,RO.0000059,
+RO.0000085,has function,"a relation between an independent continuant (the bearer) and a function, in which the function specifically depends on the bearer for its existence",,,,False,False,True,RO.0000085,
+RO.0000086,has quality,"a relation between an independent continuant (the bearer) and a quality, in which the quality specifically depends on the bearer for its existence",,,,False,False,True,RO.0000086,
+RO.0000087,has role,"a relation between an independent continuant (the bearer) and a role, in which the role specifically depends on the bearer for its existence",,,,False,False,True,RO.0000087,
+RO.0000091,has disposition,"a relation between an independent continuant (the bearer) and a disposition, in which the disposition specifically depends on the bearer for its existence",,,,False,False,True,RO.0000091,
+RO.0001000,derives from,"a relation between two distinct material entities, the new entity and the old entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity",RO.0001001,derives into,ro,False,False,True,RO.0001000,
+RO.0001001,derives into,"a relation between two distinct material entities, the old entity and the new entity, in which the new entity begins to exist when the old entity ceases to exist, and the new entity inherits the significant portion of the matter of the old entity",,,,False,False,True,RO.0001001,
+RO.0001022,has allergic trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is not part of the host, and is considered harmless to non-allergic hosts, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",,,,False,False,True,RO.0001022,
+RO.0001023,has autoimmune trigger,"A relation between a condition (a phenotype or disease) of a host and a material entity, in which the material entity is part of the host itself, and the condition results in pathological processes that include an abnormally strong immune response against the material entity.",,,,False,False,True,RO.0001023,
+RO.0001025,located in,"a relation between two independent continuants, the target and the location, in which the target is entirely within the location",,,,False,False,True,RO.0001025,
+RO.0002001,aligned with,,,,,False,False,True,RO.0002001,
+RO.0002002,has 2D boundary,"a relation between a material entity and a 2D immaterial entity (the boundary), in which the boundary delimits the material entity",,,,False,False,True,RO.0002002,
+RO.0002004,tracheates,The relationship that holds between a trachea or tracheole and an antomical structure that is contained in (and so provides an oxygen supply to).,,,,False,False,True,RO.0002004,
+RO.0002007,bounding layer of,"X outer_layer_of Y iff:
+. X :continuant that bearer_of some PATO:laminar
+. X part_of Y
+. exists Z :surface
+. X has_boundary Z
+. Z boundary_of Y
+
+has_boundary: http://purl.obolibrary.org/obo/RO_0002002
+boundary_of: http://purl.obolibrary.org/obo/RO_0002000",,,,False,False,True,RO.0002007,
+RO.0002008,coincident with,A relation that holds between two linear structures that are approximately parallel to each other for their entire length and where either the two structures are adjacent to each other or one is part of the other.,,,,False,False,True,RO.0002008,
+RO.0002009,cell expresses,"A relation that applies between a cell(c) and a gene(g) , where the process of 'transcription, DNA templated (GO_0006351)' is occuring in in cell c and that process has input gene g.",,,,False,False,True,RO.0002009,
+RO.0002011,regulates transport of,A relationship that holds between a  process that regulates a transport process and the entity transported by that process.,,,,False,False,True,RO.0002011,
+RO.0002012,occurrent part of,A part of relation that applies only between occurrents.,,,,False,False,True,RO.0002012,
+RO.0002017,has component activity,,,,,False,False,True,RO.0002017,
+RO.0002018,has component process,"w 'has process component' p if p and w are processes,  w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type.",,,,False,False,True,RO.0002018,
+RO.0002019,has ligand,"A relationship that holds between between a receptor and an chemical entity, typically a small molecule or peptide, that carries information between cells or compartments of a cell and which binds the receptor and regulates its effector function.",,,,False,False,True,RO.0002019,
+RO.0002020,transports,Holds between p and c when p is a transport process or transporter activity and the outcome of this p is to move c from one location to another.,,,,False,False,True,RO.0002020,
+RO.0002021,occurs across,"A relationship between a process and a barrier, where the process occurs in a region spanning the barrier. For cellular processes the barrier is typically a membrane. Examples include transport across a membrane and membrane depolarization.",,,,False,False,True,RO.0002021,
+RO.0002022,directly regulated by,,RO.0002578,directly regulates,ro,False,False,False,RO.0002578,
+RO.0002025,has effector activity,"A 'has effector activity' B if A and B are GO molecular functions (GO_0003674),  A 'has component activity' B and B is the effector (output function) of B.  Each compound function has only one effector activity.",,,,False,False,True,RO.0002025,
+RO.0002084,during which ends,,RO.0002093,ends during,ro,False,False,True,RO.0002084,
+RO.0002085,encompasses,,RO.0002092,happens during,ro,False,False,True,RO.0002085,
+RO.0002086,ends after,,,,,False,False,True,RO.0002086,
+RO.0002087,immediately preceded by,,RO.0002090,immediately precedes,ro,False,False,False,RO.0002090,
+RO.0002088,during which starts,,RO.0002091,starts during,ro,False,False,True,RO.0002088,
+RO.0002089,starts before,,,,,False,False,True,RO.0002089,
+RO.0002090,immediately precedes,,RO.0002087,immediately preceded by,passive_by,False,False,True,RO.0002090,
+RO.0002091,starts during,,,,,False,False,True,RO.0002091,
+RO.0002092,happens during,,,,,False,False,True,RO.0002092,
+RO.0002093,ends during,,,,,False,False,True,RO.0002093,
+RO.0002100,has soma location,Relation between a neuron and a material anatomical entity that its soma is part of.,,,,False,False,True,RO.0002100,
+RO.0002101,fasciculates with,"relationship between a neuron and a neuron projection bundle (e.g.- tract or nerve bundle) that one or more of its projections travels through.
+",,,,False,False,True,RO.0002101,
+RO.0002103,synapsed by,Relation between an anatomical structure (including cells) and a neuron that chemically synapses to it. ,RO.0002120,synapsed to,ro,False,False,True,RO.0002103,
+RO.0002111,releases neurotransmitter,,,,,False,False,True,RO.0002111,
+RO.0002120,synapsed to," Relation between a neuron and an anatomical structure (including cells) that it chemically synapses to.
+        ",,,,False,False,True,RO.0002120,
+RO.0002121,dendrite synapsed in,"Relation between a neuron and some structure  (e.g.- a brain region) in which its dendrite receives synaptic input.
+
+	",,,,False,False,True,RO.0002121,
+RO.0002131,overlaps,x overlaps y if and only if there exists some z such that x has part z and z part of y,,,,False,True,True,RO.0002131,
+RO.0002134,innervates,"Relation between a 'neuron projection bundle' and a region in which one or more of its component neuron projections either synapses to targets or receives synaptic input.
+T innervates some R
+Expands_to: T has_fasciculating_neuron_projection that synapse_in some R.",,,,False,False,True,RO.0002134,
+RO.0002151,partially overlaps,"x partially overlaps y iff there exists some z such that z is part of x and z is part of y, and it is also the case that neither x is part of y or y is part of x",,,,False,False,True,RO.0002151,
+RO.0002158,shares ancestor with,two individual entities d1 and d2 stand in a shares_ancestor_with relation if and only if there exists some a such that d1 derived_by_descent_from a and d2 derived_by_descent_from a.,,,,False,True,True,RO.0002158,
+RO.0002163,spatially disjoint from,A is spatially_disjoint_from B if and only if they have no parts in common,,,,False,False,True,RO.0002163,
+RO.0002170,connected to,"a is connected to b if and only if a and b are discrete structure, and there exists some connecting structure c, such that c connects a and b",,,,False,True,True,RO.0002170,
+RO.0002176,connects,"c connects a if and only if there exist some b such that a and b are similar parts of the same system, and c connects b, specifically, c connects a with b. When one structure connects two others it unites some aspect of the function or role they play within the system.",,,,False,True,True,RO.0002176,
+RO.0002178,supplies,"Relation between an arterial structure and another structure, where the arterial structure acts as a conduit channeling fluid, substance or energy.",,,,False,False,True,RO.0002178,
+RO.0002179,drains,"Relation between an collecting structure and another structure, where the collecting structure acts as a conduit channeling fluid, substance or energy away from the other structure.",,,,False,False,True,RO.0002179,
+RO.0002180,has component,"w 'has component' p if w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type.",,,,False,False,True,RO.0002180,
+RO.0002200,has phenotype,"A relationship that holds between a biological entity and a phenotype. Here a phenotype is construed broadly as any kind of quality of an organism part, a collection of these qualities, or a change in quality or qualities (e.g. abnormally increased temperature). The subject of this relationship can be an organism (where the organism has the phenotype, i.e. the qualities inhere in parts of this organism), a genomic entity such as a gene or genotype (if modifications of the gene or the genotype causes the phenotype), or a condition such as a disease (such that if the condition inheres in an organism, then the organism has the phenotype).",,,,False,False,True,RO.0002200,
+RO.0002202,develops from,x develops from y if and only if either (a) x directly develops from y or (b) there exists some z such that x directly develops from z and z develops from y,RO.0002203,develops into,ro,False,False,True,RO.0002202,
+RO.0002203,develops into,inverse of develops from,,,,False,False,True,RO.0002203,
+RO.0002205,has gene product,x has gene product y if and only if x is a gene (SO:0000704) that participates in some gene expression process (GO:0010467) where the output of that process is either y or something that is ribosomally translated from y,,,,False,False,True,RO.0002205,
+RO.0002206,expressed in,"x expressed in y if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and the transcription of x is part of the gene expression process (iii) x is a mature gene product such as a protein, and x was translated or otherwise processes from a transcript that was transcribed as part of this gene expression process",RO.0002292,expresses,ro,False,False,False,RO.0002292,
+RO.0002207,directly develops from,"Candidate definition: x directly_develops from y if and only if there exists some developmental process (GO:0032502) p such that x and y both participate in p, and x is the output of p and y is the input of p, and a substantial portion of the matter of x comes from y, and the start of x is coincident with or after the end of y.",RO.0002210,directly develops into,ro,False,False,True,RO.0002207,
+RO.0002209,has parasitoid,inverse of parasitoid of,,,,False,False,True,RO.0002209,
+RO.0002210,directly develops into,inverse of directly develops from,,,,False,False,True,RO.0002210,
+RO.0002211,regulates,"p regulates q iff p is causally upstream of q, the execution of p is not constant and varies according to specific conditions, and p influences the rate or magnitude of execution of q due to an effect either on some enabler of q or some enabler of a part of q.",RO.0002334,regulated by,ro,False,False,True,RO.0002211,
+RO.0002212,negatively regulates,"p negatively regulates q iff p regulates q, and p decreases the rate or magnitude of execution of q.",RO.0002335,negatively regulated by,ro,False,False,True,RO.0002212,
+RO.0002213,positively regulates,"p positively regulates q iff p regulates q, and p increases the rate or magnitude of execution of q.",RO.0002336,positively regulated by,ro,False,False,True,RO.0002213,
+RO.0002214,has prototype,"x has prototype y if and only if x is an instance of C and y is a prototypical instance of C. For example, every instance of heart, both normal and abnormal is related by the has prototype relation to some instance of a ""canonical"" heart, which participates in blood circulation.",,,,False,False,True,RO.0002214,
+RO.0002219,surrounded by,"x surrounded_by y if and only if (1) x is adjacent to y and for every region r that is adjacent to x, r overlaps y (2) the shared boundary between x and y occupies the majority of the outermost boundary of x",RO.0002221,surrounds,ro,False,False,False,RO.0002221,
+RO.0002221,surrounds,inverse of surrounded by,RO.0002219,surrounded by,passive_by,False,False,True,RO.0002221,
+RO.0002222,temporally related to,,,,,False,False,True,RO.0002222,
+RO.0002223,starts,inverse of starts with,RO.0002224,starts with,ro,False,False,True,RO.0002223,
+RO.0002224,starts with,"x starts with y if and only if x has part y and the time point at which x starts is equivalent to the time point at which y starts. Formally: α(y) = α(x) ∧ ω(y) < ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,,,False,False,True,RO.0002224,
+RO.0002226,develops in,x develops_in y if x is located in y whilst x is developing,,,,False,False,True,RO.0002226,
+RO.0002227,obligate parasite of,A sub-relation of parasite-of in which the parasite that cannot complete its life cycle without a host.,,,,False,False,True,RO.0002227,
+RO.0002229,ends,inverse of ends with,RO.0002230,ends with,ro,False,False,True,RO.0002229,
+RO.0002230,ends with,"x ends with y if and only if x has part y and the time point at which x ends is equivalent to the time point at which y ends. Formally: α(y) > α(x) ∧ ω(y) = ω(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point.",,,,False,False,True,RO.0002230,
+RO.0002231,has start location,x 'has starts location' y if and only if there exists some process z such that x 'starts with' z and z 'occurs in' y,,,,False,False,True,RO.0002231,
+RO.0002232,has end location,x 'has end location' y if and only if there exists some process z such that x 'ends with' z and z 'occurs in' y,,,,False,False,True,RO.0002232,
+RO.0002233,has input,"p has input c iff: p is a process, c is a material entity, c is a participant in p, c is present at the start of p, and the state of c is modified during p.",,,,False,False,True,RO.0002233,
+RO.0002234,has output,"p has output c iff c is a participant in p, c is present at the end of p, and c is not present in the same state at the beginning of p.",,,,False,False,True,RO.0002234,
+RO.0002238,has component participant,X 'has component participant' Y means X 'has participant' Y and there is a cardinality constraint that specifies the numbers of Ys.,,,,False,False,True,RO.0002238,
+RO.0002240,has exposure receptor,"A broad relationship between an exposure event or process and any entity (e.g., an organism, organism population, or an organism part) that interacts with an exposure stimulus during the exposure event.",,,,False,False,True,RO.0002240,
+RO.0002241,has exposure stressor,"A broad relationship between an exposure event or process and any agent, stimulus, activity, or event that causes stress or tension on an organism and interacts with an exposure receptor during an exposure event.",,,,False,False,True,RO.0002241,
+RO.0002242,has exposure route,A broad relationship between an exposure event or process and a process by which the exposure stressor comes into contact with the exposure receptor,,,,False,False,True,RO.0002242,
+RO.0002245,over-expressed in,"g is over-expressed in t iff g is expressed in t, and the expression level of g is increased relative to some background.",,,,False,False,True,RO.0002245,
+RO.0002246,under-expressed in,"g is under-expressed in t iff g is expressed in t, and the expression level of g is decreased relative to some background.",,,,False,False,True,RO.0002246,
+RO.0002248,has active ingredient,"A relationship that holds between a substance and a chemical entity, if the chemical entity is part of the substance, and the chemical entity forms the biologically active component of the substance.",,,,False,False,True,RO.0002248,
+RO.0002252,connecting branch of,"b connecting-branch-of s iff b is connected to s, and there exists some tree-like structure t such that the mereological sum of b plus s is either the same as t or a branching-part-of t.",RO.0002253,has connecting branch,ro,False,False,False,RO.0002253,
+RO.0002253,has connecting branch,inverse of connecting branch of,RO.0002252,connecting branch of,has_of,False,False,True,RO.0002253,
+RO.0002255,developmentally contributes to,inverse of has developmental contribution from,,,,False,False,True,RO.0002255,
+RO.0002256,developmentally induced by,"t1 induced_by t2 if there is a process of developmental induction (GO:0031128) with t1 and t2 as interacting participants. t2 causes t1 to change its fate from a precursor material anatomical entity type T to T', where T' develops_from T",RO.0002257,developmentally induces,ro,False,False,False,RO.0002257,
+RO.0002257,developmentally induces,Inverse of developmentally induced by,RO.0002256,developmentally induced by,passive_by,False,False,True,RO.0002257,
+RO.0002258,developmentally preceded by,"Candidate definition: x developmentally related to y if and only if there exists some developmental process (GO:0032502) p such that x and y both participates in p, and x is the output of p and y is the input of p",RO.0002286,developmentally succeeded by,ro,False,False,True,RO.0002258,
+RO.0002260,has biological role,c has-biological-role r iff c has-role r and r is a biological role (CHEBI:24432),,,,False,False,True,RO.0002260,
+RO.0002261,has application role,c has-application-role r iff c has-role r and r is an application role (CHEBI:33232),,,,False,False,True,RO.0002261,
+RO.0002262,has chemical role,c has-chemical-role r iff c has-role r and r is a chemical role (CHEBI:51086),,,,False,False,True,RO.0002262,
+RO.0002263,acts upstream of,"c acts upstream of p if and only if c enables some f that is involved in p' and p' occurs chronologically before p, is not part of p, and affects the execution of p. c is a material entity and f, p, p' are processes.",,,,False,False,True,RO.0002263,
+RO.0002286,developmentally succeeded by,Inverse of developmentally preceded by,,,,False,False,True,RO.0002286,
+RO.0002291,ubiquitously expressed in,"x is ubiquitously expressed in y if and only if x is expressed in y, and the majority of cells in y express x",RO.0002293,ubiquitously expresses,ro,False,False,False,RO.0002293,
+RO.0002292,expresses,"y expresses x if and only if there is a gene expression process (GO:0010467) that occurs in y, and one of the following holds: (i) x is a gene, and x is transcribed into a transcript as part of the gene expression process (ii) x is a transcript, and x was transcribed from a gene as part of the gene expression process (iii) x is a mature gene product (protein or RNA), and x was translated or otherwise processed from a transcript that was transcribed as part of the gene expression process.",RO.0002206,expressed in,passive_prep,False,False,True,RO.0002292,
+RO.0002293,ubiquitously expresses,inverse of ubiquiotously expressed in,RO.0002291,ubiquitously expressed in,passive_prep,False,False,True,RO.0002293,
+RO.0002303,has habitat,"x 'has habitat' y if and only if: x is an organism, y is a habitat, and y can sustain and allow the growth of a population of xs.",,,,False,False,True,RO.0002303,
+RO.0002309,has exposure stimulus,"A relationship between an exposure event or process and any agent, stimulus, activity, or event that causally effects an organism and interacts with an exposure receptor during an exposure event.",,,,False,False,True,RO.0002309,
+RO.0002320,evolutionarily related to,A relationship that holds via some environmental process,,,,False,False,True,RO.0002320,
+RO.0002321,ecologically related to,A relationship that is mediated in some way by the environment or environmental feature (ENVO:00002297),,,,False,False,True,RO.0002321,
+RO.0002322,confers advantage in,,,,,False,False,True,RO.0002322,
+RO.0002323,mereotopologically related to,A mereological relationship or a topological relationship,,,,False,False,True,RO.0002323,
+RO.0002324,developmentally related to,A relationship that holds between entities participating in some developmental process (GO:0032502),,,,False,False,True,RO.0002324,
+RO.0002325,colocalizes with,a colocalizes_with b if and only if  a is transiently or peripherally associated with b[GO].,,,,False,False,True,RO.0002325,
+RO.0002326,contributes to,,,,,False,False,True,RO.0002326,
+RO.0002327,enables,c enables p iff c is capable of p and c acts to execute p.,RO.0002333,enabled by,ro,False,False,True,RO.0002327,
+RO.0002328,functionally related to,"A grouping relationship for any relationship directly involving a function, or that holds because of a function of one of the related entities.",,,,False,False,True,RO.0002328,
+RO.0002330,genomically related to,"holds between two entities when some genome-level process such as gene expression is involved. This includes transcriptional, spliceosomal events. These relations can be used between either macromolecule entities (such as regions of nucleic acid) or between their abstract informational counterparts.",,,,False,False,True,RO.0002330,
+RO.0002331,involved in,"c involved_in p if and only if c enables some process p', and p' is part of p",,,,False,False,True,RO.0002331,
+RO.0002332,regulates levels of,p regulates levels of c if p regulates some amount (PATO:0000070) of c,,,,False,False,True,RO.0002332,
+RO.0002333,enabled by,inverse of enables,RO.0002327,enables,passive_by,False,False,False,RO.0002327,
+RO.0002334,regulated by,inverse of regulates,RO.0002211,regulates,passive_by,False,False,False,RO.0002211,
+RO.0002335,negatively regulated by,inverse of negatively regulates,RO.0002212,negatively regulates,passive_by,False,False,False,RO.0002212,
+RO.0002336,positively regulated by,inverse of positively regulates,RO.0002213,positively regulates,passive_by,False,False,False,RO.0002213,
+RO.0002340,imports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the start location of c is part of the region that surrounds s.",,,,False,False,True,RO.0002340,
+RO.0002345,exports,"Holds between p and c when p is a transportation or localization process and the outcome of this process is to move c to a destination that is part of some s, where the end location of c is part of the region that surrounds s.",,,,False,False,True,RO.0002345,
+RO.0002351,has member,has member is a mereological relation between a collection and an item.,,,,False,False,True,RO.0002351,
+RO.0002360,has dendrite location,,,,,False,False,True,RO.0002360,
+RO.0002371,attached to,"a is attached to b if and only if a and b are discrete objects or object parts, and there are physical connections between a and b such that a force pulling a will move b, or a force pulling b will move a",,,,False,True,True,RO.0002371,
+RO.0002372,has muscle origin,"m has_muscle_origin s iff m is attached_to s, and it is the case that when m contracts, s does not move. The site of the origin tends to be more proximal and have greater mass than what the other end attaches to.",,,,False,False,True,RO.0002372,
+RO.0002373,has muscle insertion,"m has_muscle_insertion s iff m is attaches_to s, and it is the case that when m contracts, s moves. Insertions are usually connections of muscle via tendon to bone.",,,,False,False,True,RO.0002373,
+RO.0002380,branching part of,x is a branching part of y if and only if x is part of y and x is connected directly or indirectly to the main stem of y,RO.0002569,has branching part,ro,False,False,False,RO.0002569,
+RO.0002400,has direct input,"p has direct input c iff c is a participant in p, c is present at the start of p, and the state of c is modified during p.",,,,False,False,True,RO.0002400,
+RO.0002407,indirectly positively regulates,p indirectly positively regulates q iff p is indirectly causally upstream of q and p positively regulates q.,,,,False,False,True,RO.0002407,
+RO.0002409,indirectly negatively regulates,p indirectly negatively regulates q iff p is indirectly causally upstream of q and p negatively regulates q.,,,,False,False,True,RO.0002409,
+RO.0002410,causally related to,"relation that links two events, processes, states, or objects such that one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly or wholly responsible for the effect, and the effect is partly or wholly dependent on the cause.",,,,False,False,True,RO.0002410,
+RO.0002413,provides input for,"p provides input for q iff p is immediately causally upstream of q, and there exists some c such that p has_output c and q has_input c.",,,,False,False,True,RO.0002413,
+RO.0002424,differs in,,,,,False,False,True,RO.0002424,
+RO.0002432,is active in,"c executes activity in d if and only if c enables p and p occurs_in d.  Assuming no action at a distance by gene products, if a gene product enables (is capable of) a process that occurs in some structure, it must have at least some part in that structure.",,,,False,False,True,RO.0002432,
+RO.0002434,interacts with,A relationship that holds between two entities in which the processes executed by the two entities are causally connected.,,,,False,True,True,RO.0002434,
+RO.0002435,genetically interacts with,,,,,False,True,True,RO.0002435,
+RO.0002436,molecularly interacts with,An interaction relationship in which the two partners are molecular entities that directly physically interact with each other for example via a stable binding interaction or a brief interaction during which one modifies the other.,,,,False,True,True,RO.0002436,
+RO.0002437,biotically interacts with,An interaction relationship in which at least one of the partners is an organism and the other is either an organism or an abiotic entity with which the organism interacts.,,,,False,True,True,RO.0002437,
+RO.0002438,trophically interacts with,An interaction relationship in which the partners are related via a feeding relationship.,,,,False,False,True,RO.0002438,
+RO.0002439,preys on,"An interaction relationship involving a predation process, where the subject kills the target in order to eat it or to feed to siblings, offspring or group members",RO.0002458,preyed upon by,ro,False,False,True,RO.0002439,
+RO.0002440,symbiotically interacts with,A biotic interaction in which the two organisms live together in more or less intimate association.,,,,False,True,True,RO.0002440,
+RO.0002441,commensually interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which one benefits and the other is unaffected (GO).,,,,False,False,True,RO.0002441,
+RO.0002442,mutualistically interacts with,An interaction relationship between two organisms living together in more or less intimate association in a relationship in which both organisms benefit from each other (GO).,,,,False,False,True,RO.0002442,
+RO.0002445,parasitized by,Inverse of parasite of,,,,False,False,True,RO.0002445,
+RO.0002451,transmitted by,A relationship that holds between a disease and organism,,,,False,False,True,RO.0002451,
+RO.0002452,has symptom,A relation that holds between a disease or an organism and a phenotype,,,,False,False,True,RO.0002452,
+RO.0002454,has host,"X 'has host' y if and only if: x is an organism, y is an organism, and x can live on the surface of or within the body of y",,,,False,False,True,RO.0002454,
+RO.0002455,pollinates,,RO.0002456,pollinated by,ro,False,False,True,RO.0002455,
+RO.0002456,pollinated by,,RO.0002455,pollinates,passive_by,False,False,False,RO.0002455,
+RO.0002457,acquires nutrients from,Inverse of provides nutrients for,RO.0002469,provides nutrients for,ro,False,False,True,RO.0002457,
+RO.0002458,preyed upon by,inverse of preys on,,,,False,False,True,RO.0002458,
+RO.0002459,is vector for,,RO.0002460,has vector,ro,False,False,True,RO.0002459,
+RO.0002460,has vector,,,,,False,False,True,RO.0002460,
+RO.0002465,is symbiosis,,,,,False,False,True,RO.0002465,
+RO.0002466,is commensalism,,,,,False,False,True,RO.0002466,
+RO.0002467,is mutualism,,,,,False,False,True,RO.0002467,
+RO.0002468,is parasitism,,,,,False,False,True,RO.0002468,
+RO.0002469,provides nutrients for,A biotic interaction where a material entity provides nutrition for an organism.,,,,False,False,True,RO.0002469,
+RO.0002470,eats,A biotic interaction where one organism consumes a material entity through a type of mouth or other oral opening.,RO.0002471,is eaten by,passive_by,False,False,True,RO.0002470,
+RO.0002471,is eaten by,Inverse of eats,RO.0002470,eats,passive_by,False,False,False,RO.0002470,
+RO.0002472,is evidence for,"A relationship between a piece of evidence a and some entity b, where b is  an information content entity, material entity or process, and 
+the a supports either the existence of b, or the truth value of b.",RO.0002558,has evidence,ro,False,False,True,RO.0002472,
+RO.0002473,composed primarily of,x composed_primarily_of y if and only if more than half of the mass of x is made from y or units of the same type as y.,,,,False,False,True,RO.0002473,
+RO.0002480,ubiquitinates,An interaction relation between x and y in which x catalyzes a reaction in which one or more ubiquitin groups are added to y,,,,False,False,True,RO.0002480,
+RO.0002481,is kinase activity,,,,,False,False,True,RO.0002481,
+RO.0002482,is ubiquitination,,,,,False,False,True,RO.0002482,
+RO.0002485,receives input from,,RO.0002486,sends output to,ro,False,False,True,RO.0002485,
+RO.0002486,sends output to,,,,,False,False,True,RO.0002486,
+RO.0002488,existence starts during,x existence starts during y if and only if the time point at which x starts is after or equivalent to the time point at which y starts and before or equivalent to the time point at which y ends. Formally: x existence starts during y iff α(x) >= α(y) & α(x) <= ω(y).,,,,False,False,True,RO.0002488,
+RO.0002489,existence starts with,x starts ends with y if and only if the time point at which x starts is equivalent to the time point at which y starts. Formally: x existence starts with y iff α(x) = α(y).,,,,False,False,True,RO.0002489,
+RO.0002490,existence overlaps,x existence overlaps y if and only if either (a) the start of x is part of y or (b) the end of x is part of y. Formally: x existence starts and ends during y iff (α(x) >= α(y) & α(x) <= ω(y)) OR (ω(x) <= ω(y) & ω(x) >= α(y)),,,,False,False,True,RO.0002490,
+RO.0002492,existence ends during,x existence ends during y if and only if the time point at which x ends is before or equivalent to the time point at which y ends and after or equivalent to the point at which y starts. Formally: x existence ends during y iff ω(x) <= ω(y) and ω(x) >= α(y).,,,,False,False,True,RO.0002492,
+RO.0002493,existence ends with,x existence ends with y if and only if the time point at which x ends is equivalent to the time point at which y ends. Formally: x existence ends with y iff ω(x) = ω(y).,,,,False,False,True,RO.0002493,
+RO.0002502,depends on,,,,,False,False,True,RO.0002502,
+RO.0002505,has intermediate,"p has intermediate c if and only if p has parts p1, p2 and p1 has output c, and p2 has input c",,,,False,False,True,RO.0002505,
+RO.0002507,determined by,"s determined by f if and only if s is a type of system, and f is a material entity that is part of s, such that f exerts a strong causal influence on the functioning of s, and the removal of f would cause the collapse of s.",RO.0002508,determines,ro,False,False,False,RO.0002508,
+RO.0002508,determines,inverse of determined by,RO.0002507,determined by,passive_by,False,False,True,RO.0002508,
+RO.0002510,transcribed from,x is transcribed from y if and only if x is synthesized from template y,RO.0002511,transcribed to,ro,False,False,True,RO.0002510,
+RO.0002511,transcribed to,inverse of transcribed from,,,,False,False,True,RO.0002511,
+RO.0002513,ribosomally translates to,inverse of ribosomal translation of,,,,False,False,True,RO.0002513,
+RO.0002514,sequentially related to,A relation that holds between two entities that have the property of being sequences or having sequences. ,,,,False,False,True,RO.0002514,
+RO.0002516,has start sequence,"x has start sequence y if the start of x is identical to the start of y, and x has y as a subsequence",,,,False,False,True,RO.0002516,
+RO.0002518,has end sequence,"x has end sequence y if the end of x is identical to the end of y, and x has y as a subsequence",,,,False,False,True,RO.0002518,
+RO.0002522,bounds sequence of,"x bounds the sequence of y iff the upstream-most part of x is upstream of or coincident with the upstream-most part of y, and the downstream-most part of x is downstream of or coincident with the downstream-most part of y",,,,False,False,True,RO.0002522,
+RO.0002524,has subsequence,x has subsequence y iff all of the sequence parts of y are sequence parts of x,RO.0002525,is subsequence of,ro,False,False,True,RO.0002524,
+RO.0002525,is subsequence of,inverse of has subsequence,,,,False,False,True,RO.0002525,
+RO.0002551,has skeleton,A relation between a segment or subdivision of an organism and the maximal subdivision of material entities that provides structural support for that segment or subdivision.,,,,False,False,True,RO.0002551,
+RO.0002554,hyperparasitized by,inverse of hyperparasite of,,,,False,False,True,RO.0002554,
+RO.0002557,has pathogen,A host interaction where the smaller of the two members of a symbiosis causes a disease in the larger member,,,,False,False,True,RO.0002557,
+RO.0002558,has evidence,inverse of is evidence for,,,,False,False,True,RO.0002558,
+RO.0002559,causally influenced by,,RO.0002566,causally influences,ro,False,False,False,RO.0002566,
+RO.0002566,causally influences,"The entity or characteristic A is causally upstream of the entity or characteristic B, A having an effect on B. An entity corresponds to any biological type of entity as long as a mass is measurable. A characteristic corresponds to a particular specificity of an entity (e.g., phenotype, shape, size).",RO.0002559,causally influenced by,passive_by,False,False,True,RO.0002566,
+RO.0002567,biomechanically related to,A relation that holds between elements of a musculoskeletal system or its analogs.,,,,False,False,True,RO.0002567,
+RO.0002568,has muscle antagonist,"m1 has_muscle_antagonist m2 iff m1 has_muscle_insertion s, m2 has_muscle_insection s, m1 acts in opposition to m2, and m2 is responsible for returning the structure to its initial position.",,,,False,False,True,RO.0002568,
+RO.0002569,has branching part,inverse of branching part of,RO.0002380,branching part of,has_of,False,False,True,RO.0002569,
+RO.0002573,has modifier,A relation that holds between an attribute or a qualifier and another attribute.,,,,False,False,True,RO.0002573,
+RO.0002578,directly regulates,p directly regulates q iff p is immediately causally upstream of q and p regulates q.,RO.0002022,directly regulated by,passive_by,False,False,True,RO.0002578,
+RO.0002596,capable of regulating,"Holds between c and p if and only if c is capable of some activity a, and a regulates p.",,,,False,False,True,RO.0002596,
+RO.0002607,is marker for,"c is marker for d iff the presence or occurrence of d is correlated with the presence of occurrence of c, and the observation of c is used to infer the presence or occurrence of d. Note that this does not imply that c and d are in a direct causal relationship, as it may be the case that there is a third entity e that stands in a direct causal relationship with c and d.",,,,False,False,True,RO.0002607,
+RO.0002610,correlated with,"A relationship that holds between two entities, where the entities exhibit a statistical dependence relationship. The entities may be statistical variables, or they may be other kinds of entities such as diseases, chemical entities or processes.",,,,False,False,True,RO.0002610,
+RO.0002615,has model,Inverse of is-model-of,,,,False,False,True,RO.0002615,
+RO.0002618,visits,,RO.0002619,visited by,ro,False,False,True,RO.0002618,
+RO.0002619,visited by,,RO.0002618,visits,passive_by,False,False,False,RO.0002618,
+RO.0002622,visits flowers of,,,,,False,False,True,RO.0002622,
+RO.0002624,lays eggs in,,,,,False,False,True,RO.0002624,
+RO.0002626,kills,,RO.0002627,is killed by,ro,False,False,True,RO.0002626,
+RO.0002627,is killed by,,RO.0002626,kills,passive_by,False,False,False,RO.0002626,
+RO.0002629,directly positively regulates,"p directly positively regulates q iff p is immediately causally upstream of q, and p positively regulates q.",,,,False,False,True,RO.0002629,
+RO.0002630,directly negatively regulates,"p directly negatively regulates q iff p is immediately causally upstream of q, and p negatively regulates q.",,,,False,False,True,RO.0002630,
+RO.0002633,has ectoparasite,inverse of ectoparasite of,,,,False,False,True,RO.0002633,
+RO.0002635,has endoparasite,,,,,False,False,True,RO.0002635,
+RO.0002637,has mesoparasite,inverse of mesoparasite of,,,,False,False,True,RO.0002637,
+RO.0002639,has intercellular endoparasite,inverse of intercellular endoparasite of,,,,False,False,True,RO.0002639,
+RO.0002641,has intracellular endoparasite,inverse of intracellular endoparasite of,,,,False,False,True,RO.0002641,
+RO.0002801,co-roosts with,"Two or more individuals sharing the same roost site (cave, mine, tree or tree hollow, animal burrow, leaf tent, rock crack, space in man-made structure, etc.). Individuals that are sharing a communal roost may be said to be co-roosting. The roost may be either a day roost where the individuals rest during daytime hours, or a night roost where individuals roost to feed, groom, or rest in between flights and/or foraging bouts.  Communal roosting as thus defined is an umbrella term within which different specialized types -- which are not mutually exclusive -- may be recognized based on taxonomy and the temporal and spatial relationships of the individuals that are co-roosting.",,,,False,True,True,RO.0002801,
+RO.0002803,has reservoir host,inverse of reservoir host of,,,,False,False,True,RO.0002803,
+RO.0003001,produced by,a produced_by b iff some process that occurs_in b has_output a.,PEL.000026,produces,passive_by,False,False,False,PEL.000026,
+RO.0003002,represses expression of,Holds between entity A (a transcription factor) and a nucleic acid B if and only if A down-regulates the expression of B. The nucleic acid can be a gene or an mRNA.,,,,False,False,True,RO.0003002,
+RO.0003003,increases expression of,Holds between entity A (a transcription factor) and nucleic acid B if and only if A up-regulates the expression of B. The nucleic acid can be a gene or mRNA.,,,,False,False,True,RO.0003003,
+RO.0003303,causes condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some causal role for the condition.",,,,False,False,True,RO.0003303,
+RO.0003304,contributes to condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the entity has some contributing role that influences the condition.",,,,False,False,True,RO.0003304,
+RO.0003307,ameliorates condition,"A relationship between an entity (e.g. a genotype, genetic variation, chemical, or environmental exposure) and a condition (a phenotype or disease), where the presence of the entity reduces or eliminates some or all aspects of the condition.",,,,False,False,True,RO.0003307,
+RO.0003308,correlated with condition,A relationship between an entity and a condition (phenotype or disease) with which it exhibits a statistical dependence relationship.,,,,False,False,True,RO.0003308,
+RO.0003309,exacerbates condition,"A relationship between an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) and a condition (a phenotype or disease), where the presence of the entity worsens some or all aspects of the condition.",,,,False,False,True,RO.0003309,
+RO.0003310,condition ameliorated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are reduced or eliminated by the presence of the entity.",,,,False,False,True,RO.0003310,
+RO.0003311,condition exacerbated by,"A relationship between a condition (a phenotype or disease) and an entity (e.g. a chemical, environmental exposure, or some form of genetic variation) where some or all aspects of the condition are worsened by the presence of the entity.",,,,False,False,True,RO.0003311,
+RO.0004008,has primary output,"p has primary output c if (a) p has output c and (b) the goal of process is to modify, produce, or transform c.",,,,False,False,True,RO.0004008,
+RO.0004009,has primary input,"p has primary input c if (a) p has input c and (b) the goal of process is to modify, consume, or transform c.",,,,False,False,True,RO.0004009,
+RO.0004026,disease has location,A relationship between a disease and an anatomical entity where the disease has one or more features that are located in that entity.,,,,False,False,True,RO.0004026,
+RO.0004029,disease has feature,"A relationship between a disease and some feature of that disease, where the feature is either a phenotype or an isolated disease.",,,,False,False,True,RO.0004029,
+RO.0004031,enables subfunction,"Holds between an entity and an process P where the entity enables some larger compound process, and that larger process has-part P.",,,,False,False,True,RO.0004031,
+RO.0007000,has driver,"A relation between two entities, in which one of the entities is any natural or human-influenced factor that directly or indirectly causes a change in the other entity.",,,,False,False,True,RO.0007000,
+RO.0007001,has disease driver,"A relation between an entity and a disease of a host, in which the entity is not part of the host itself, and the condition results in pathological processes.",,,,False,False,True,RO.0007001,
+RO.0008502,has epiphyte,inverse of epiphyte of,,,,False,False,True,RO.0008502,
+RO.0008504,kleptoparasitized by,inverse of kleptoparasite of,,,,False,False,True,RO.0008504,
+RO.0008505,creates habitat for,An interaction relationship wherein one organism creates a structure or environment that is lived in by another organism.,,,,False,False,True,RO.0008505,
+RO.0008506,ecologically co-occurs with,An interaction relationship describing organisms that often occur together at the same time and space or in the same environment.,,,,False,True,True,RO.0008506,
+RO.0008507,lays eggs on,An interaction relationship in which organism a lays eggs on the outside surface of organism b. Organism b is neither helped nor harmed in the process of egg laying or incubation.,,,,False,False,True,RO.0008507,
+RO.0008509,has roost,"x 'has roost' y if and only if: x is an organism, y is a habitat, and y can support rest behaviors x.",,,,False,False,True,RO.0008509,
+RO.0009001,has substance added,"""has substance added"" is a relation existing between a (physical) entity and a substance in which the entity has had the substance added to it at some point in time.",,,,False,False,True,RO.0009001,
+RO.0009002,has substance removed,"""has substance removed"" is a relation existing between two physical entities in which the first entity has had the second entity (a substance) removed from it at some point in time.",,,,False,False,True,RO.0009002,
+RO.0009003,immersed in,"""immersed in"" is a relation between a (physical) entity and a fluid substance in which the entity is wholely or substantially surrounded by the substance.",,,,False,False,True,RO.0009003,
+RO.0009004,has consumer,'has consumer' is a relation between a material entity and an organism in which the former can normally be digested or otherwise absorbed by the latter without immediate or persistent ill effect.,,,,False,False,True,RO.0009004,
+RO.0009006,assay measures characteristic,"A relation between an assay and a characteristic, in which the assay generates a data item which is a measure of a characteristic.",,,,False,False,True,RO.0009006,
+RO.0010001,generically depends on,A generically dependent continuant *b* generically depends on an independent continuant *c* at time *t* means: there inheres in *c* a specifically deendent continuant which concretizes *b* at *t*.,RO.0010002,is carrier of,ro,False,False,True,RO.0010001,
+RO.0010002,is carrier of,*b* is carrier of *c* at time *t* if and only if *c* *g-depends on* *b* at *t*,,,,False,False,True,RO.0010002,
+RO.0011002,regulates activity of,"The entity A has an activity that regulates an activity of the entity B. For example, A and B are gene products where the catalytic activity of A regulates the kinase activity of B.",,,,False,False,True,RO.0011002,
+RO.0011003,regulates quantity of,The entity A has an activity that regulates the quantity or abundance or concentration of the entity B.,,,,False,False,True,RO.0011003,
+RO.0011014,destabilizes quantity of,An entity A directly interacts with B and A has an activity that decreases the amount of an entity B by degradating it.,,,,False,False,True,RO.0011014,
+RO.0011015,stabilizes quantity of,An entity A physically interacts with B and A has an activity that increases the amount of an entity B by stabilizing it.,,,,False,False,True,RO.0011015,
+RO.0012010,removes input for,"p removes input for q iff p is causally upstream of q, there exists some c such that p has_input c and q has_input c, p reduces the levels of c, and c is rate limiting for execution of q.",,,,False,False,True,RO.0012010,
+RO.0012012,indirectly regulates,p indirectly regulates q iff p is indirectly causally upstream of q and p regulates q.,,,,False,False,True,RO.0012012,
+RO.0015001,has exemplar data,A relation between a material entity and some data in which the data is taken as exemplifying the material entity.,,,,False,False,True,RO.0015001,
+RO.0015010,has relative magnitude,,,,,False,False,True,RO.0015010,
+RO.0015011,has cross section,"s3 has_cross_section s3 if and only if : there exists some 2d plane that intersects the bearer of s3, and the impression of s3 upon that plane has shape quality s2.",,,,False,False,True,RO.0015011,
+RO.0016002,has disease,A relationship that holds between an organism and a disease. Here a disease is construed broadly as a disposition to undergo pathological processes that exists in an organism because of one or more disorders in that organism.,,,,False,False,True,RO.0016002,
+RO.0016004,has exposure medium,"X has exposure medium Y if X is an exposure event (process), Y is a material entity, and the stimulus for X is transmitted or carried in Y.",,,,False,False,True,RO.0016004,
+RO.0017001,device utilizes material,"X device utilizes material Y means X and Y are material entities, and X is capable of some process P that has input Y.",,,,False,False,True,RO.0017001,
+RO.0017003,positively correlated with,A relation between entities in which one increases or decreases as the other does the same.,,,,False,True,True,RO.0017003,
+RO.0017004,negatively correlated with,A relation between entities in which one increases as the other decreases.,,,,False,True,True,RO.0017004,
+RO.0017005,contains measured amount,A relation between a container and measurement datum that specifies the actual amount of material in the container.,,,,False,False,True,RO.0017005,
+RO.0017006,has maximum capacity,A relation that relates a container to a measurement datum that specifies the maximum capacity of the container. Capacity can refer to either weight or volume.,,,,False,False,True,RO.0017006,
+RO.0017008,owns,A primitive relation that holds between entities x and y in which y is at x's full disposal.,RO.0017009,is owned by,ro,False,False,True,RO.0017008,
+RO.0017009,is owned by,Inverse of the owns relation.,RO.0017008,owns,passive_by,False,False,False,RO.0017008,
+RO.0018001,is myristoyltransferase activity,Helper relation for OWL definition of RO:0018002 myristoylates,,,,False,False,True,RO.0018001,
+RO.0018003,myristoylated by,inverse of myristoylates,,,,False,False,True,RO.0018003,
+RO.0018027,is agonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor activates some activity of the receptor,,,,False,False,True,RO.0018027,
+RO.0018029,is antagonist of,a relation between a ligand (material entity) and a receptor (material entity) that implies the binding of the ligand to the receptor reduces some activity of the receptor to basal level,,,,False,False,True,RO.0018029,
+RO.0018036,is tautomer of,"Two chemicals are tautomers if they can be readily interconverted.
+
+This commonly refers to prototropy in which a hydrogen's position is changed, such as between ketones and enols. This is also often observed in heterocyclic rings, e.g., ones containing nitrogens and/or have aryl functional groups containing heteroatoms.",,,,False,True,True,RO.0018036,
+RO.0018038,has functional parent,"Chemical A has functional parent Chemical B if there is chemical transformation through which chemical B can be produced from chemical A. 
+
+For example, the relationship between a salt and a freebased compound is a ""has functional parent"" relationship.",,,,False,False,True,RO.0018038,
+RO.0018039,is enantiomer of,"Chemicals A and B are enantiomers if they share the same molecular graph except the change of the configuration of substituents around exactly one chiral center.
+
+A chemical with no chiral centers can not have an enantiomer. A chemical with multiple chiral centers can have multiple enantiomers, but its enantiomers are not themselves enantiomers (they are diastereomers).",,,,False,True,True,RO.0018039,
+RO.0018040,has parent hydride,Chemical A has parent hydride Chemical B if there exists a molecular graphical transformation where functional groups on A are replaced with hydrogens in order to yield B.,,,,False,False,True,RO.0018040,
+RO.0019000,regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) regulates characteristic (C) iff: P results in the existence of C OR affects the intensity or magnitude of C.,,,,False,False,True,RO.0019000,
+RO.0019001,positively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) positively regulates characteristic (C) iff: P results in an increase in the intensity or magnitude of C.,,,,False,False,True,RO.0019001,
+RO.0019002,negatively regulates characteristic,A relationship that holds between a process and a characteristic in which process (P) negatively regulates characteristic (C) iff: P results in a decrease in the intensity or magnitude of C.,,,,False,False,True,RO.0019002,
+RO.0020202,has numerator,A relationship between a ratio or proportion and its dividend.,,,,False,False,True,RO.0020202,
+RO.0020203,has denominator,A relationship between a ratio or proportion and its divisor.,,,,False,False,True,RO.0020203,
+PEL.000020,interrupts,stop the continuous progress of (an activity or process),PEL.000058,interrupted by,passive_by,False,False,True,PEL.000020,
+PEL.000034,stimulates,excite to activity or growth or to greater activity transactivate,PEL.000069,stimulated by,passive_by,False,False,True,PEL.000034,
+PEL.000038,activated by,converse of 'activates': X is activated by Y when Y activates X,PEL.000000,activates,passive_by,False,False,False,PEL.000000,
+PEL.000039,actuated by,converse of 'actuates': X is actuated by Y when Y actuates X,PEL.000001,actuates,passive_by,False,False,False,PEL.000001,
+PEL.000040,associated with,converse of 'associates': X is associated with Y when Y associates X,,,,False,True,True,PEL.000040,
+PEL.000041,bound by,converse of 'binds to': X is bound by Y when Y binds to X,,,,False,True,True,PEL.000041,
+PEL.000042,caused by,converse of 'causes': X is caused by Y when Y causes X,PEL.000004,causes,passive_by,False,False,False,PEL.000004,
+PEL.000043,constrained by,converse of 'constrains': X is constrained by Y when Y constrains X,PEL.000005,constrains,passive_by,False,False,False,PEL.000005,
+PEL.000044,controlled by,converse of 'controls': X is controlled by Y when Y controls X,PEL.000006,controls,passive_by,False,False,False,PEL.000006,
+PEL.000045,degraded by,converse of 'degrades': X is degraded by Y when Y degrades X,PEL.000007,degrades,passive_by,False,False,False,PEL.000007,
+PEL.000046,destroyed by,converse of 'destroys': X is destroyed by Y when Y destroys X,PEL.000008,destroys,passive_by,False,False,False,PEL.000008,
+PEL.000047,directed by,converse of 'directs': X is directed by Y when Y directs X,PEL.000009,directs,passive_by,False,False,False,PEL.000009,
+PEL.000048,downregulated by,converse of 'downregulates': X is downregulated by Y when Y downregulates X,PEL.000010,downregulates,passive_by,False,False,False,PEL.000010,
+PEL.000049,enhanced by,converse of 'enhances': X is enhanced by Y when Y enhances X,PEL.000011,enhances,passive_by,False,False,False,PEL.000011,
+PEL.000050,facilitated by,converse of 'facilitates': X is facilitated by Y when Y facilitates X,PEL.000012,facilitates,passive_by,False,False,False,PEL.000012,
+PEL.000051,forced by,converse of 'forces': X is forced by Y when Y forces X,PEL.000013,forces,passive_by,False,False,False,PEL.000013,
+PEL.000052,hindered by,converse of 'hinders': X is hindered by Y when Y hinders X,PEL.000014,hinders,passive_by,False,False,False,PEL.000014,
+PEL.000053,inactivated by,converse of 'inactivates': X is inactivated by Y when Y inactivates X,PEL.000015,inactivates,passive_by,False,False,False,PEL.000015,
+PEL.000054,increased by,converse of 'increases': X is increased by Y when Y increases X,PEL.000016,increases,passive_by,False,False,False,PEL.000016,
+PEL.000055,induced by,converse of 'induces': X is induced by Y when Y induces X,PEL.000017,induces,passive_by,False,False,False,PEL.000017,
+PEL.000056,inhibited by,converse of 'inhibits': X is inhibited by Y when Y inhibits X,PEL.000018,inhibits,passive_by,False,False,False,PEL.000018,
+PEL.000057,initiated by,converse of 'initiates': X is initiated by Y when Y initiates X,PEL.000019,initiates,passive_by,False,False,False,PEL.000019,
+PEL.000058,interrupted by,converse of 'interrupts': X is interrupted by Y when Y interrupts X,PEL.000020,interrupts,passive_by,False,False,False,PEL.000020,
+PEL.000059,limited by,converse of 'limits': X is limited by Y when Y limits X,PEL.000022,limits,passive_by,False,False,False,PEL.000022,
+PEL.000060,mentioned by,converse of 'mentions': X is mentioned by Y when Y mentions X,PEL.000023,mentions,passive_by,False,False,False,PEL.000023,
+PEL.000061,overproduced by,converse of 'overproduces': X is overproduced by Y when Y overproduces X,PEL.000024,overproduces,passive_by,False,False,False,PEL.000024,
+PEL.000062,phosphorylated by,converse of 'phosphorylates': X is phosphorylated by Y when Y phosphorylates X,PEL.000025,phosphorylates,passive_by,False,False,False,PEL.000025,
+PEL.000063,promoted by,converse of 'promotes': X is promoted by Y when Y promotes X,PEL.000027,promotes,passive_by,False,False,False,PEL.000027,
+PEL.000064,provoked by,converse of 'provokes': X is provoked by Y when Y provokes X,PEL.000028,provokes,passive_by,False,False,False,PEL.000028,
+PEL.000065,reduced by,converse of 'reduces': X is reduced by Y when Y reduces X,PEL.000029,reduces,passive_by,False,False,False,PEL.000029,
+PEL.000066,repressed by,converse of 'represses': X is repressed by Y when Y represses X,PEL.000030,represses,passive_by,False,False,False,PEL.000030,
+PEL.000067,reverted by,converse of 'reverts': X is reverted by Y when Y reverts X,PEL.000031,reverts,passive_by,False,False,False,PEL.000031,
+PEL.000068,secreted by,converse of 'secretes': X is secreted by Y when Y secretes X,PEL.000032,secretes,passive_by,False,False,False,PEL.000032,
+PEL.000069,stimulated by,converse of 'stimulates': X is stimulated by Y when Y stimulates X,PEL.000034,stimulates,passive_by,False,False,False,PEL.000034,
+PEL.000070,suppressed by,converse of 'suppresses': X is suppressed by Y when Y suppresses X,PEL.000035,suppresses,passive_by,False,False,False,PEL.000035,
+PEL.000071,triggered by,converse of 'triggers': X is triggered by Y when Y triggers X,PEL.000036,triggers,passive_by,False,False,False,PEL.000036,
+PEL.000072,upregulated by,converse of 'upregulates': X is upregulated by Y when Y upregulates X,PEL.000037,upregulates,passive_by,False,False,False,PEL.000037,
diff --git a/docs/index.md b/docs/index.md
index 516c043..45623ab 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -8,6 +8,7 @@ The current **package version** is defined in `pyproject.toml` (see the `[projec
 
 - **[Run scripts & CLIs](user_guide/run_scripts_and_cli.md)** — `pelinker-fit`, `pelinker-serves`, `pelinker-link-files`, and how they connect to scripts under `run/` (including OOV / anomaly analysis).
 - **[Vector representations](user_guide/vector_representation.md)** — how `texts_to_vrep` turns text and transformer layers into pooled embeddings for sliding token windows.
+- **[Gold evaluation](user_guide/evaluation.md)** — the human-verified gold set: canonical predicate vocabulary, LLM pre-annotation, annotator agreement, and the reference baselines.
 - **API Reference** — auto-generated from package docstrings (`pelinker`).
 - **Repository run guide** — long-form tables and preprocessing live in [`run/README.md`](https://github.com/growgraph/pelinker/blob/main/run/README.md) on GitHub.
 
diff --git a/docs/user_guide/api.md b/docs/user_guide/api.md
new file mode 100644
index 0000000..dd8fc30
--- /dev/null
+++ b/docs/user_guide/api.md
@@ -0,0 +1,77 @@
+# HTTP API
+
+`pelinker-serves` (`pelinker.cli.server`) loads one fitted linker and serves it over
+FastAPI. The default port is **8599**. Configuration uses Hydra overrides (`host`, `port`,
+`model_file_spec`, `thr_score`, `use_gpu`, `cors_allow_origins`); defaults are in
+`pelinker/conf/server.yaml`. Interactive docs are at `/docs` while the server runs.
+
+```bash
+uv run pelinker-serves model_file_spec=<models>/pelinker.pubmedbert.run1 port=8599
+```
+
+## Routes
+
+| Route | Method | Returns |
+|---|---|---|
+| `/health` | GET | `{"status": "ok"}` |
+| `/info` | GET | Loaded artifact: path, embedding metadata, spaCy pipeline, KB config, vocabulary size, cluster count, transform config, `emits_direction` |
+| `/model` | GET | Embedding metadata and spaCy pipeline name |
+| `/link` | POST | Linked predicate mentions |
+| `/link/debug` | POST | The same, plus per-mention diagnostics |
+
+## `POST /link`
+
+Request body — `text` or `texts` is required:
+
+| Field | Type | Meaning |
+|---|---|---|
+| `text` | string | One document |
+| `texts` | list of strings | Several documents; each must be non-empty |
+| `thr_score` | float, optional | Minimum cluster membership score; defaults to the server's `thr_score` |
+| `use_gpu` | bool, optional | Encode on CUDA; defaults to the server's `use_gpu` |
+| `max_length` | int, optional (1–8192) | Encoder chunk length |
+
+The response is `{"entities": [...]}`, one row per linked mention:
+
+| Field | Meaning |
+|---|---|
+| `mention` | The mention text |
+| `a`, `b` | Character offsets in the document |
+| `itext` | Index of the document in `texts` |
+| `ichunk` | Encoder chunk the mention came from |
+| `entity_id_predicted` | The **KB-out id** of the mention's cluster (`<kb>::C0007`). This is the linker's primary output: a cluster may merge several input-KB entries or hold one sense of an entry that is split across clusters |
+| `score` | Cluster membership probability; rows below `thr_score` are dropped |
+| `direction_predicted` | `forward`, `inverse` or `symmetric`, relative to the canonical relation the cluster represents. Present only when the linker was fitted with a class view (`emits_direction` in `/info`) |
+
+Mentions that fall in no cluster (HDBSCAN noise), or that a screener rejects, are
+abstentions: they are omitted rather than linked to the nearest entity.
+
+### From a KB-out id to the input KB
+
+A KB-out id belongs to one fit. To reach the ids of the input KB, read the fit's catalog
+(`linker_fit.kb_out.json`, written under the fit's `report_path`):
+
+- `pelinker.kb.kb_out.kb_out_to_kb_in_map(catalog)` gives each KB-out id's dominant
+  input-KB id;
+- `pelinker.kb.kb_out.kb_out_to_reldir_map(catalog)` gives (dominant input-KB id,
+  dominant direction).
+
+The dominant id is a projection. The catalog's `components` and `entity_membership`
+record everything a cluster absorbed.
+
+## `POST /link/debug`
+
+The same inputs as `/link`, plus:
+
+| Field | Type | Meaning |
+|---|---|---|
+| `include_entity_anomaly_metrics` | bool | Add PCA residual, Mahalanobis, spectral entropy and projection scores to each entity row |
+| `kb_validation` | bool | Add KB lemma-match fields to each entity row |
+
+The response adds `mention_anomaly`: one diagnostic row per extracted mention, including
+the ones a screener rejected.
+
+## Smoke client
+
+`run/smoke_server.py` calls every route against a running server; see
+[Run scripts & CLIs](run_scripts_and_cli.md).
diff --git a/docs/user_guide/evaluation.md b/docs/user_guide/evaluation.md
new file mode 100644
index 0000000..d9314c7
--- /dev/null
+++ b/docs/user_guide/evaluation.md
@@ -0,0 +1,341 @@
+# Gold evaluation
+
+The clustering metrics (DBCV, ARI) say how coherent the fitted manifold is against the
+pipeline's own weak labels. They cannot say whether a linked mention is *right*. This
+guide covers the other track: a human-verified gold set of annotated predicate mentions,
+and reference baselines scored against it through one harness.
+
+Everything here lives in `run/eval/` (drivers) and `pelinker/eval/` (library code). The
+linker itself never calls an LLM; only this evaluation path does.
+
+## Install and credentials
+
+```bash
+uv sync --extra dev --extra eval
+```
+
+The `eval` extra installs every provider SDK behind `pelinker.eval.llm`. Each provider
+reads its own credential from the environment:
+
+| Provider | Credential |
+|----------|------------|
+| `gemini` (default) | `GEMINI_API_KEY` or `GOOGLE_API_KEY` |
+| `anthropic` | `ANTHROPIC_API_KEY` |
+| `openai` | `OPENAI_API_KEY` |
+
+Responses are cached on disk, keyed on provider, model and both prompts. Re-runs, parser
+fixes and re-scoring are free, and a finished annotation batch is reproducible from the
+cache alone.
+
+## The id space: canonical, with direction carried separately
+
+The property KB holds both members of many converse pairs — "regulates" and "regulated
+by", "has part" and "part of" — as separate entries. If an annotator could use either, a
+passive mention would have two equally valid encodings, and the gold would record
+disagreements that are really just two names for one relation.
+
+So one member of each pair is elected **canonical**, and orientation moves into a separate
+`direction` field (`forward`, `inverse`, `symmetric`, `na`). Annotation offers canonical
+labels only. Scoring folds every system's answer the same way, so a baseline that answers
+"regulated by" is scored on the relation it picked rather than on which name it used.
+
+**Symmetric relations** ("interacts with", "overlaps", "connected to") have no converse:
+read either way they are the same relation, so their only valid direction is `symmetric`.
+They are flagged `is_symmetric`, marked `symmetric` in the annotation prompt, and an
+annotator's oriented answer on one is set to `symmetric` and flagged `direction_coerced`.
+
+This is why the whole pipeline consumes the **pairs** KB, and why the tools refuse a KB
+that predates the derivation instead of quietly falling back:
+
+```
+properties.synthesis.2.csv          # merged KB
+  → properties.synthesis.2.inverse.csv   # + owl:inverseOf, the derivation's INPUT
+    → properties.synthesis.2.pairs.csv   # + is_canonical / canonical_entity_id / is_symmetric
+```
+
+## Step 0 — derive converse pairs, and review them
+
+The full lineage of the KB files — sources, hand-curated files, the generated pairs KB and
+what each edit invalidates — is diagrammed in `data/README.md` in the repository.
+
+```bash
+uv run python run/preprocessing/derive_inverse_pairs.py \
+  --kb-csv-path data/derived/properties.synthesis.2.inverse.csv \
+  --output-path data/derived/properties.synthesis.2.pairs.csv \
+  --review-csv data/curated/inverse_pairs.review.csv \
+  --ro-csv-path data/derived/properties.ro.csv \
+  --symmetric-csv data/curated/symmetric.csv
+```
+
+`is_symmetric` is joined from the RO extraction (`extract_properties_ro.py`), which counts
+a property as symmetric when RO declares `owl:SymmetricProperty` or defines it by the chain
+`inverse(P) ∘ P`, plus the curated `data/curated/symmetric.csv` for entries RO does not
+cover. The surface-form rules never pair a symmetric entry.
+
+Pairs come from four rules, recorded per row in `inverse_source`. Only `ro` (a declared
+`owl:inverseOf`) is a fact about the ontology; `has_of`, `passive_by` and `passive_prep`
+are surface-form proposals, marked `needs_review`.
+
+Unreviewed proposals are written to `<output>.pending_review.csv`. A curator fills in
+`verdict`:
+
+- **accept** — confirmed converses; the pair stands and `needs_review` clears.
+- **reject** — not converses; the link is removed and each member becomes its own
+  canonical form again.
+
+Pass the filled sheet back as `--review-csv` and re-run. Review before annotating, because
+a wrong pair merges two distinct relations onto one canonical id and nothing downstream
+can see it: prompts, gold and scores all agree with the mistake. `annotate_llm.py` warns
+while any pair is still unreviewed.
+
+## Step 1 — sample the documents
+
+```bash
+uv run python run/eval/sample_gold.py \
+  --input-text-table-path <held-out>.tsv.gz \
+  --exclude-corpus <fit corpus>.tsv.gz \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --output-dir <workdir>/gold
+```
+
+The sampler works in three stages:
+
+1. **Eligibility gate.** Only plausible English research abstracts are drawn:
+   - publication year from 1990 (`--min-year`);
+   - length within `--min-chars` / `--max-chars`, and at least `--min-sentences`
+     sentences, which excludes citation strings, news items and full texts;
+   - an English function-word share (`--min-english-share`);
+   - no exact duplicates, and no document from the fit corpus (matched on `mag` and on
+     text hash).
+
+   Each dropped row is counted by reason in `sampling_report.batch<k>.json`.
+2. **Mentions.** Each eligible abstract is parsed, and its KB mentions are detected with
+   the same definition the fit uses. Verb labels are matched from the dependency parse,
+   one per verb; other labels are matched lexically; each site keeps one label. A raw
+   lemma-window count mostly measures abstract length, because nouns ("control",
+   "increase") match labels too.
+3. **Strata.** Documents are grouped by year tercile × verb-mention density (`none` /
+   `low` / `high`) × `tail`, which marks an abstract holding a label outside the pool's
+   `--tail-top-k` most frequent.
+   - Tail strata are over-allocated by `--tail-weight`, so rare relations reach the gold
+     set sooner.
+   - The draw is random within each stratum, and each document records its inclusion
+     probability `p_incl`, so rates can be reweighted to the pool.
+   - About `--none-fraction` of documents are negative controls with no detected mention.
+
+**Grow the sample in batches.** Re-running with `--extend` appends a batch:
+- documents already drawn are excluded;
+- `doc_id`s stay stable;
+- roles are assigned per batch in the `--n-primary` / `--n-double` / `--n-reserve`
+  proportions, so the lockbox holds as the set grows;
+- the pool's detected mentions are cached in `pool_mentions.csv.gz` while the KB and gate
+  are unchanged.
+
+After each batch, and after annotating it, run the audit:
+
+```bash
+uv run python run/eval/audit_sample.py --gold-dir <workdir>/gold \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv
+```
+
+It re-checks eligibility, reports how strongly density still tracks length, lists
+documents and negative controls with no gold hit, and measures label concentration. It
+also reports coverage: canonical labels reaching `--min-mentions` gold hits, out of those
+the pool can supply, plus a cumulative curve per batch. Stop adding batches when that
+curve levels off.
+
+Every document is assigned a role:
+
+| Role | Default size | Purpose |
+|------|--------------|---------|
+| `primary` | `--n-primary` | the reported gold slice, annotated once |
+| `double` | `--n-double` | annotated twice, for the agreement (κ) check |
+| `reserve` | `--n-reserve` | held back, not annotated |
+
+A drawn document without any identifier is an error rather than a row: a gold span whose
+document cannot be cited is not usable evidence.
+
+## Step 2 — pre-annotate with an LLM
+
+```bash
+uv run python run/eval/annotate_llm.py \
+  --sample-dir <workdir>/gold \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --output-path <workdir>/gold/gold.llm-a.json \
+  --roles primary,double
+```
+
+Three contracts make the output auditable:
+
+- **The model answers with labels, never ids.** Ids mean nothing to a language model, and
+  transcribing them fails silently; an unmatched label is a logged rejection instead.
+- **The model never emits character offsets.** It quotes the surface verbatim and gives a
+  1-based occurrence index; the driver resolves offsets itself and rejects what does not
+  resolve. Rejections land in `<output>.report.json` — they are never dropped silently.
+- **A converse label is recovered, not discarded.** The prompt offers canonical labels,
+  but the decoder accepts the whole KB: an answer of "expressed in" maps onto the
+  canonical id with `direction` flipped and is flagged. Dropping those would remove
+  inverse-direction mentions specifically — the population the directionality work needs.
+
+**Scope: verb mentions.** A hit on a verb-predicate label whose span contains no verb
+(for example "association" or "inhibition") is rejected as `non_verbal`. The linker is
+trained on verb mentions only. Labels that are not verb predicates ("has part") keep
+their noun surfaces. The check runs after the response cache, so `--no-verbal-only`
+re-decodes without any LLM calls.
+
+**Paraphrases are kept and tagged.** Each hit carries `surface_anchored`:
+- `true` when the surface uses the label's own wording ("increased" for `increases`);
+- `false` for a paraphrase ("leads to" for `causes`).
+
+Paraphrases are valid gold. The linker's training is anchored on label lemmas, so
+paraphrases are scored as a separate slice that measures generalisation. The tag is
+computed after the cache.
+
+Use `--dry-run` to see request sizes without spending anything.
+
+**Truncated answers are never cached.** A response the provider cut off at `--max-tokens`
+is reported as `truncated` in `<output>.report.json`, and the document is left out of
+the output. It is not written as an empty document, because that would read as "the
+annotator found nothing". Nothing is cached for it, so re-running with a larger limit
+retries only those documents. A mention the model repeats after every occurrence is
+already claimed is reported as `duplicate_occurrence`. `surface_not_found` means the
+quoted text is not in the abstract at all.
+
+## Step 3 — a second annotator for agreement and recall
+
+Run it again with a different **model family**, over the same roles:
+
+```bash
+uv run python run/eval/annotate_llm.py \
+  --sample-dir <workdir>/gold \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --output-path <workdir>/gold/gold.llm-b.json \
+  --provider openai --model <model-id> --reasoning-effort low --max-tokens 16000 \
+  --roles primary,double
+```
+
+The second annotator does two jobs. It gives κ, and it raises recall: verified gold only
+ever contains spans that one of the annotators proposed. Running it over `double` alone
+would leave the reporting documents bounded by a single annotator's recall.
+
+A second checkpoint of the same family mostly measures itself; κ between families is the
+informative number.
+
+For a reasoning model, pin `--reasoning-effort` and give `--max-tokens` headroom, because
+the hidden reasoning counts against the same limit as the answer. The effort is part of
+the cache key and of the default annotator tag (`<model>@<effort>`), so each setting is
+its own run. Try `--limit 3` first and read `usage` in the cached responses under
+`.llm_cache/` to size the limit.
+
+## Step 4 — agreement, review sheet, adjudication
+
+```bash
+uv run python run/eval/gold_review_sheet.py agreement \
+  --gold <workdir>/gold/gold.llm-a.json --other <workdir>/gold/gold.llm-b.json \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --equivalences data/curated/equivalences.csv
+
+uv run python run/eval/gold_review_sheet.py export \
+  --gold <workdir>/gold/gold.llm-a.json --other <workdir>/gold/gold.llm-b.json \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --equivalences data/curated/equivalences.csv \
+  --output <workdir>/gold/review.tsv
+```
+
+κ is reported on entity id and on direction, over the documents **both** annotators
+covered — `n_docs_shared` says how many. A span only one annotator marked enters the
+entity-id κ as an explicit `∅` on the other side, in both directions, so missing a mention
+and inventing one are penalized alike.
+
+With `--kb-csv-path`, κ is also reported **folded** (`kappa_entity_id_folded`). Ids are
+compared in the reference inventory's tiers 0 and 1: converse members fold onto their
+canonical member, and `--equivalences` adds the identities the KB implies without
+declaring (`data/curated/equivalences.csv`). Two annotators who name one relation two ways
+then agree. The raw κ stays beside it.
+
+The review TSV has one row per candidate span with a marked context window. Rows carry an
+`origin`: `gold` from the first annotator, `other` for spans only the second proposed —
+without those, verified gold could never exceed the first annotator's recall. On documents
+the second annotator never saw, the comparison columns read `not-covered` rather than
+looking like a disagreement. Each row shows the other annotator's `other_entity_id`,
+`other_label` and `other_direction`. `agrees` compares the two ids in the folded space.
+`agrees_direction` is filled only where the relation agrees (a symmetric relation agrees in
+any direction), so an `agrees=True`, `agrees_direction=False` row is a pure direction
+dispute.
+
+Fill in `verdict` (`accept` / `reject` / `fix`) on **every** row, with `fix_entity_id` and
+`fix_direction` where the verdict is `fix`; anything else is an error, so nothing is kept
+by omission. Then:
+
+```bash
+uv run python run/eval/gold_review_sheet.py import \
+  --gold <workdir>/gold/gold.llm-a.json --sheet <workdir>/gold/review.tsv \
+  --other <workdir>/gold/gold.llm-b.json \
+  --verified-by <name> --output <workdir>/gold/gold.verified.json
+```
+
+Kept hits become `source="human"` with the adjudicator as `annotator`, and record their
+`verdict`. `--other` is required whenever an `origin=other` row was accepted, since the
+hit and its argument spans live in that file.
+
+Import also writes `<output>.verification.json`. It holds the accept / fix / reject
+counts and the **correction rate** (fixed plus rejected, over reviewed candidates), split
+into anchored and paraphrase slices. `audit_sample.py --gold-file gold.verified.json`
+includes it.
+
+**Describing the gold set.** Every candidate is reviewed, but candidates come only from
+the LLM annotators. The accurate description is therefore *LLM pre-annotated, every
+candidate manually verified*, not *manually annotated*: a mention both annotators missed
+is not in gold. Report the annotator κ and the correction rate alongside it. A baseline
+LLM linker should come from a model family that did not pre-annotate the gold, or it is
+scored against its own kind.
+
+## Step 5 — score the baselines
+
+```bash
+uv run python run/eval/run_baselines.py \
+  --gold <workdir>/gold/gold.verified.json \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --report-dir <workdir>/eval-runs/<run-name> \
+  --systems lexical,encoder \
+  --model-path <models>/pelinker.pubmedbert.1
+```
+
+| System | What it does | Regimes |
+|--------|--------------|---------|
+| `lexical` | lemma match to KB labels — the floor | end-to-end + linking-only |
+| `encoder` | sentence-encoder cosine top-1 over `label: description` | linking-only (end-to-end reuses the lexical spans) |
+| `llm` | constrained choice over the KB, disk-cached | linking-only |
+| `linker` | a fitted PELinker artifact, via the KB-out → KB-in bridge | end-to-end + linking-only |
+
+Two regimes are reported separately. **End-to-end** scores span detection and linking
+together. **Linking-only** gives the system the gold spans, isolating the id decision from
+span proposal.
+
+Configure the `llm` baseline with a model that did **not** annotate the gold, or the
+comparison is circular. Nothing enforces this.
+
+Results go to `baseline_results.json` / `.csv` under `--report-dir`. Keep that directory
+outside the repository: measured numbers belong with the measurement writeup.
+
+## Step 6 — corpus and gold statistics
+
+```bash
+uv run python run/eval/dataset_stats.py \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --gold <workdir>/gold/gold.verified.json \
+  --report-dir <workdir>/eval-runs/dataset-stats
+```
+
+Any subset of corpus / KB / fit report / gold may be passed; each contributes its section
+to `dataset_stats.json`.
+
+## Reading a result
+
+- `entity_accuracy` is **undefined**, not zero, when `n_id_comparable` is 0 — most often
+  because a linker artifact emitted minted KB-out ids and no id bridge was available.
+- `accuracy_overall` in the linking-only regime counts abstentions as wrong;
+  `accuracy_on_predicted` does not. Report both, or the headline flatters whichever system
+  abstains most.
+- Direction is annotated in gold but not scored by the baselines: they assign an id and
+  nothing else.
diff --git a/docs/user_guide/run_scripts_and_cli.md b/docs/user_guide/run_scripts_and_cli.md
index cec5cd0..d436c9d 100644
--- a/docs/user_guide/run_scripts_and_cli.md
+++ b/docs/user_guide/run_scripts_and_cli.md
@@ -7,10 +7,15 @@ This page is the **high-level map** for training, serving, batch linking, and of
 From the repository root, use **`uv`** so dependencies match `uv.lock`:
 
 ```bash
-uv sync --all-groups
-uv run spacy download en_core_web_trf
+uv sync --extra dev
 ```
 
+The `dev` extra pins the spaCy `en_core_web_lg` model as a dependency, so a separate
+`spacy download` is unnecessary — and would not survive the next `uv sync`, which prunes
+anything not declared. These are **extras**, not dependency groups: name every one you
+want in a single command (`uv sync --extra dev --extra eval`), because the ones you leave
+out are uninstalled.
+
 Documentation site builds (optional):
 
 ```bash
@@ -25,8 +30,17 @@ uv run mkdocs serve
 | `uv run pelinker-fit` | `pelinker.cli.fit` | Corpus embedding (optional) + `Linker.fit` → serialized artifact (`.gz`). Hydra overrides; defaults in `pelinker/conf/fit.yaml`. Mention load flags (`drop_rare_entities`, `max_mentions_per_entity`, `clustering_sample_rows`) align with model selection. |
 | `uv run pelinker-serves` | `pelinker.cli.server` | FastAPI server: `/health`, `/info`, `/model`, `/link`, `/link/debug`. Defaults in `pelinker/conf/server.yaml`. |
 | `uv run pelinker-link-files` | `pelinker.cli.link_files` | Batch `Linker.predict` on UTF-8 files or JSON documents; optional JSON report and **mention-level anomaly dump** for OOV workflows. |
+| `uv run pelinker-model-selection` | `pelinker.cli.model_selection` | Grid search over embedding backbone × layers on a directory of mention parquets; writes the run report and heatmaps. Like the other searches and `pelinker-fit`, it scores ARI in a *class view* of the KB — `--class-view` (default `reldir`: canonical relation + direction) with `--class-kb-path` pointing at the pairs KB; `raw` scores against the matched labels. See `run/README.md` § Class views. |
+| `uv run pelinker-dim-selection` | `pelinker.cli.dim_selection` | `(pca_components, umap_dim)` search on one parquet, same metric stack. `--persist-labels` also writes the per-draw labels that cluster-stability analysis needs. |
+| `uv run pelinker-scale-curve` | `pelinker.cli.scale_curve` | Measures how the chosen `min_cluster_size` moves with mention-frame size; writes `scale_curve.json` for `pelinker-fit scale_curve_path=`. |
+| `uv run pelinker-replot` | `pelinker.cli.replot` | Regenerates model-selection figures from an existing report directory — no re-embedding, no re-clustering. |
+
+Each has an equivalent module invocation, e.g. `uv run python -m pelinker.cli.fit`.
 
-Equivalent module invocations: `uv run python -m pelinker.cli.fit`, `pelinker.cli.server`, `pelinker.cli.link_files`.
+The hyperparameters chosen by the searches reach a fit through `selection_report=` and
+`scale_curve_path=` rather than being retyped; `pelinker-fit` records which source won
+under `min_cluster_size_provenance`. Note also that `pelinker-fit` defaults to
+`pipeline=embed_only` — pass `pipeline=both` for an end-to-end train.
 
 ### Batch linking (`pelinker-link-files`)
 
@@ -47,14 +61,15 @@ Plain text files are one document per file; JSON inputs support `text` plus opti
 
 | Area | Contents |
 |------|-----------|
-| **Root** | `embed_kb_corpus.py`, `test_server.py`, `loop.embed.kb.corpus.sh`, `loop.fit.sh` |
-| **`preprocessing/`** | GO / RO property extraction and merge → synthesis KB CSVs |
-| **`analysis/`** | `model_selection.py` (thin shim → `pelinker.model_selection`; embedding grid + metrics; canonical run report `model_selection.run_report.json.gz`), `select_diverse_entities.py`, **`oov_analysis.py`** (fit report + OOV dump → figures), **`replot_dbcv_ari_scatter.py`** (PNG from an existing `results_grid_per_sample.csv`) |
-| **`obsolete/`** | Deprecated experiments (not maintained) |
+| **Root** | `embed_kb_corpus.py` (standalone stage A), `smoke_server.py` (HTTP smoke tests), `loop.embed.kb.corpus.sh`, `loop.fit.sh` |
+| **`preprocessing/`** | GO / RO property extraction and merge → synthesis KB CSVs, then `derive_inverse_pairs.py` → the canonical pairs KB |
+| **`analysis/`** | `compact_predict_study.py` (legacy vs compact predict arms), `cluster_stability.py` (cluster identity across draws), `direction_diagnostic.py` (converse-pair collisions), `oov_analysis.py` (fit report + OOV dump → figures), `replot_fit.py` (figures from a fit report), `select_diverse_entities.py` |
+| **`eval/`** | The gold pipeline: sampling, LLM pre-annotation, review round trip with κ, reference baselines — see [Gold evaluation](evaluation.md) |
 
 Always invoke scripts with **`uv run python …`** (see project rules) so the locked environment is used.
 
 ## See also
 
 - **[Vector representations](vector_representation.md)** — encoder + spaCy window path (`texts_to_vrep`).
-- **API Reference** — generated module pages (`pelinker.model`, `pelinker.analysis`, …).
+- **[Gold evaluation](evaluation.md)** — the human-verified gold set and the reference baselines.
+- **API Reference** — generated module pages (`pelinker.model`, `pelinker.search`, …).
diff --git a/docs/user_guide/vector_representation.md b/docs/user_guide/vector_representation.md
index c446d97..6ba6f6f 100644
--- a/docs/user_guide/vector_representation.md
+++ b/docs/user_guide/vector_representation.md
@@ -1,6 +1,6 @@
 # Vector representations (`texts_to_vrep`)
 
-This page describes the pipeline implemented by `texts_to_vrep` in `pelinker.util`: how raw strings become layer activations, how those align with spaCy token windows, and what objects you get back. Generated API entries live under **API Reference** (e.g. `pelinker.util`, `pelinker.onto`).
+This page describes the pipeline implemented by `texts_to_vrep` in `pelinker.text.embed`: how raw strings become layer activations, how those align with spaCy token windows, and what objects you get back. Generated API entries live under **API Reference** (e.g. `pelinker.text.embed`, `pelinker.core.onto`).
 
 ## Purpose
 
@@ -73,4 +73,4 @@ The transformer is run **once**; the loop over `word_modes` only recomputes spaC
 ## See also
 
 - **[Run scripts & CLIs](run_scripts_and_cli.md)** — training, server, and batch linking entry points.
-- Generated module pages: **API Reference** → `pelinker.util`, `pelinker.onto`.
+- Generated module pages: **API Reference** → `pelinker.text.embed`, `pelinker.text.chunking`, `pelinker.core.onto`.
diff --git a/install-cuda121.sh b/install-cuda121.sh
deleted file mode 100755
index 4e4d7e2..0000000
--- a/install-cuda121.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/usr/bin/env bash
-set -e
-
-pip install torch torchvision torchaudio \
-  --index-url https://download.pytorch.org/whl/cu121
-
-pip install cupy-cuda12x
-
-pip install -e .
diff --git a/mkdocs.yml b/mkdocs.yml
index e6ec414..0c0700f 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -59,6 +59,8 @@ nav:
 -   User Guide:
     -   Run scripts & CLIs: user_guide/run_scripts_and_cli.md
     -   Vector representations: user_guide/vector_representation.md
+    -   Gold evaluation: user_guide/evaluation.md
+    -   HTTP API: user_guide/api.md
 -   API Reference: reference/
 
 markdown_extensions:
diff --git a/pelinker/__init__.py b/pelinker/__init__.py
index e69de29..79f8f9a 100644
--- a/pelinker/__init__.py
+++ b/pelinker/__init__.py
@@ -0,0 +1,12 @@
+"""pelinker -- property/entity linking for BERT-like models.
+
+Deliberately free of heavy imports: importing anything under ``pelinker`` must not pull in
+torch or TensorFlow. Scripts that only touch the KB, the gold schema or the evaluation
+harness pay nothing for the ML stack, and a process that has already loaded TensorFlow
+(a debugger, a notebook) can import this package without tripping the native
+torch/TensorFlow load-order conflict.
+
+That conflict is real, and it is handled where it arises:
+:func:`pelinker.core.runtime.preload_torch_before_tensorflow`, called at the two sites
+that import ``umap.parametric_umap``.
+"""
diff --git a/pelinker/analysis.py b/pelinker/analysis.py
deleted file mode 100644
index ad76591..0000000
--- a/pelinker/analysis.py
+++ /dev/null
@@ -1,1087 +0,0 @@
-from __future__ import annotations
-
-import math
-from collections.abc import Iterable, Mapping, Sequence
-from dataclasses import replace
-
-import numpy as np
-import pandas as pd
-import torch
-from pelinker.clustering_grid import (
-    SmoothedGridOptimumResult,
-    aggregate_grid_metrics,
-    solve_optimal_min_cluster_size_from_aggregated,
-)
-from pelinker.grid_export import per_combo_metrics_from_grid
-from pelinker.reporting import (
-    AllScreenerCvResult,
-    BinaryClassifierMetrics,
-    ClusteringFitMetrics,
-    MetricMeanStd,
-    NegativeScreenerInSampleMetrics,
-    PerDatapointScores,
-)
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-from sklearn.metrics import (
-    adjusted_rand_score,
-    f1_score,
-    precision_score,
-    recall_score,
-    roc_auc_score,
-)
-from sklearn.model_selection import StratifiedKFold
-
-from pelinker.config import (
-    ClusteringOptimizationConfig,
-    GridObjectiveSpec,
-    ManifoldOovScreenerConfig,
-    NegativeScreenerConfig,
-)
-from pelinker.screener.projection_screener import (
-    ManifoldOovKind,
-    make_manifold_linear_svc,
-    make_manifold_rbf_svc,
-    oov_estimator_scores,
-    pick_projection_winner_by_mean_f1,
-)
-from pelinker.screener.ambient_screener import (
-    NegativeClassScreener,
-    _linear_svc_for_embeddings,
-)
-
-
-def get_word_frequencies_from_library(
-    language: str = "en",
-    wordlist: str = "best",
-) -> object | None:
-    """
-    Get word frequency lookup object from wordfreq library.
-
-    Args:
-        language: Language code (default: "en" for English)
-        wordlist: Wordlist size - "best", "large", or "small" (default: "best")
-
-    Returns:
-        WordFrequencyLookup object with .get() method, or None if library not available
-    """
-    try:
-        from wordfreq import word_frequency  # type: ignore
-
-        # Return a callable that looks up frequencies
-        # We'll use a lazy lookup approach
-        class WordFrequencyLookup:
-            def __init__(self, lang: str, wlist: str):
-                self.lang = lang
-                self.wlist = wlist
-                self._cache: dict[str, float] = {}
-
-            def get(self, word: str, default: float = 0.0) -> float:
-                word_lower = word.lower()
-                if word_lower not in self._cache:
-                    try:
-                        self._cache[word_lower] = word_frequency(
-                            word_lower, self.lang, wordlist=self.wlist
-                        )
-                    except (KeyError, ValueError):
-                        self._cache[word_lower] = default
-                return self._cache[word_lower]
-
-            def __getitem__(self, word: str) -> float:
-                return self.get(word)
-
-        return WordFrequencyLookup(language, wordlist)  # type: ignore
-    except ImportError:
-        return None
-
-
-def _measure_label_simplicity(
-    label: str,
-    word_frequencies: Mapping[str, float],
-    stopwords: Iterable[str] = (
-        "is",
-        "of",
-        "the",
-        "a",
-        "an",
-        "to",
-        "for",
-        "or",
-        "in",
-        "has",
-    ),
-    zero_freq_penalty: float = 1e-8,
-    multiword_penalty: float = 0.2,
-    stopword_penalty: float = 0.3,
-) -> dict[str, int | float]:
-    """..."""
-
-    text = label.strip().lower()
-
-    # Handle empty labels
-    if not text:
-        return {"char_count": 0, "word_count": 0, "simplicity_score": 0.0}
-
-    words = text.split()
-    word_count = len(words)
-
-    stopword_set = set(stopwords)
-    content_words = [w for w in words if w not in stopword_set]
-    stopword_count = word_count - len(content_words)
-
-    # Handle labels with only stopwords
-    if not content_words:
-        return {
-            "char_count": len(text),
-            "word_count": word_count,
-            "simplicity_score": zero_freq_penalty,
-        }
-
-    # Get frequencies for content words
-    content_freqs = [word_frequencies.get(w, zero_freq_penalty) for w in content_words]
-
-    # Harmonic mean
-    harmonic_mean_freq = len(content_freqs) / sum(
-        1.0 / max(f, zero_freq_penalty) for f in content_freqs
-    )
-
-    # Apply penalties multiplicatively (but ensure they don't go negative)
-    penalty_factor = 1.0
-
-    if word_count > 1:
-        penalty_factor *= max(0.0, 1.0 - multiword_penalty * (word_count - 1))
-
-    if stopword_count > 0 and word_count > 1:
-        penalty_factor *= max(0.0, 1.0 - stopword_penalty * stopword_count)
-
-    simplicity_score = harmonic_mean_freq * penalty_factor
-
-    return {
-        "char_count": len(text),
-        "word_count": word_count,
-        "simplicity_score": simplicity_score,
-    }
-
-
-def compute_adjusted_rand_index(y_true: np.ndarray, y_pred: np.ndarray) -> float:
-    """
-    Compute clustering quality via adjusted Rand index (ARI).
-
-    Args:
-        y_true: True labels (e.g., property names)
-        y_pred: Predicted cluster labels
-
-    Returns:
-        ARI score.
-    """
-    # Filter out noise points (label -1) for accuracy computation
-    valid_mask = y_pred != -1
-    if not valid_mask.any():
-        return 0.0
-
-    y_true_valid = y_true[valid_mask]
-    y_pred_valid = y_pred[valid_mask]
-
-    if len(y_true_valid) == 0:
-        return 0.0
-
-    ari = adjusted_rand_score(y_true_valid, y_pred_valid)
-    return float(ari)
-
-
-def pooled_grid_solve_from_metrics_dfs(
-    metrics_dfs: Sequence[pd.DataFrame],
-    optimization_config: ClusteringOptimizationConfig | None = None,
-) -> SmoothedGridOptimumResult:
-    """
-    After all bootstrap samples have run a min_cluster_size grid, aggregate their metrics
-    once and return full smoothed-grid diagnostics (including ``y_objective`` curve).
-    """
-    if not metrics_dfs:
-        raise ValueError("metrics_dfs must be non-empty")
-    config = optimization_config or ClusteringOptimizationConfig()
-    aggregated = aggregate_grid_metrics(list(metrics_dfs))
-    return solve_optimal_min_cluster_size_from_aggregated(
-        aggregated,
-        objective=config.grid_objective,
-        method=config.optimization_method,
-        smooth_window=config.grid_smooth_window,
-        plateau_fraction=config.grid_plateau_fraction,
-        derivative_rel_tol=config.grid_derivative_rel_tol,
-        cluster_count_reward=config.grid_cluster_count_reward,
-        n_entities=config.grid_n_entities,
-    )
-
-
-def pooled_min_cluster_size_from_metrics_dfs(
-    metrics_dfs: Sequence[pd.DataFrame],
-    optimization_config: ClusteringOptimizationConfig | None = None,
-) -> tuple[int, float]:
-    """
-    After all bootstrap samples have run a min_cluster_size grid, aggregate their metrics
-    once and return the smoothed ``(chosen_min_cluster_size, raw objective mean at that grid point)``.
-    The objective is set by ``ClusteringOptimizationConfig.grid_objective`` (default: pooled
-    min–max normalized DBCV and ARI).
-    """
-    solved = pooled_grid_solve_from_metrics_dfs(metrics_dfs, optimization_config)
-    return solved.chosen_min_cluster_size, solved.score_mean_at_chosen
-
-
-def grid_solver_config(
-    optimization_config: ClusteringOptimizationConfig | None,
-    *,
-    grid_cluster_count_reward: float | None = None,
-    grid_n_entities: int | None = None,
-    grid_objective: GridObjectiveSpec | None = None,
-    optimization_method: str | None = None,
-) -> ClusteringOptimizationConfig:
-    """Merge optional solver overrides into ``optimization_config`` (or defaults)."""
-    base = optimization_config or ClusteringOptimizationConfig()
-    overrides: dict[str, object] = {}
-    if grid_cluster_count_reward is not None:
-        overrides["grid_cluster_count_reward"] = grid_cluster_count_reward
-    if grid_n_entities is not None:
-        overrides["grid_n_entities"] = grid_n_entities
-    if grid_objective is not None:
-        overrides["grid_objective"] = grid_objective
-    if optimization_method is not None:
-        overrides["optimization_method"] = optimization_method
-    return replace(base, **overrides) if overrides else base
-
-
-def grid_solver_overrides_active(
-    *,
-    optimization_config: ClusteringOptimizationConfig | None,
-    grid_cluster_count_reward: float | None,
-    grid_n_entities: int | None,
-    grid_objective: GridObjectiveSpec | None,
-    optimization_method: str | None,
-) -> bool:
-    """True when any solver knob was supplied, i.e. a re-solve was explicitly requested."""
-    return any(
-        v is not None
-        for v in (
-            optimization_config,
-            grid_cluster_count_reward,
-            grid_n_entities,
-            grid_objective,
-            optimization_method,
-        )
-    )
-
-
-def should_resolve_chosen_min_cluster_size(
-    *,
-    chosen_min_cluster_size: float | None,
-    optimization_config: ClusteringOptimizationConfig | None,
-    grid_cluster_count_reward: float | None,
-    grid_n_entities: int | None,
-    grid_objective: GridObjectiveSpec | None,
-    optimization_method: str | None,
-) -> bool:
-    """Re-solve only when no chosen value is on hand *and* solver knobs were supplied."""
-    if chosen_min_cluster_size is not None:
-        return False
-    return grid_solver_overrides_active(
-        optimization_config=optimization_config,
-        grid_cluster_count_reward=grid_cluster_count_reward,
-        grid_n_entities=grid_n_entities,
-        grid_objective=grid_objective,
-        optimization_method=optimization_method,
-    )
-
-
-def solve_pooled_grid_from_metrics_list(
-    metrics_list: list[pd.DataFrame],
-    optimization_config: ClusteringOptimizationConfig | None = None,
-    *,
-    grid_cluster_count_reward: float | None = None,
-    grid_n_entities: int | None = None,
-    grid_objective: GridObjectiveSpec | None = None,
-    optimization_method: str | None = None,
-) -> SmoothedGridOptimumResult:
-    """Pooled grid solve on per-sample metric tables; returns full diagnostics."""
-    cfg = grid_solver_config(
-        optimization_config,
-        grid_cluster_count_reward=grid_cluster_count_reward,
-        grid_n_entities=grid_n_entities,
-        grid_objective=grid_objective,
-        optimization_method=optimization_method,
-    )
-    return pooled_grid_solve_from_metrics_dfs(metrics_list, cfg)
-
-
-def solve_pooled_grid_by_combo_from_grid(
-    df_grid: pd.DataFrame,
-    optimization_config: ClusteringOptimizationConfig | None = None,
-    *,
-    grid_cluster_count_reward: float | None = None,
-    grid_n_entities: int | None = None,
-    grid_objective: GridObjectiveSpec | None = None,
-    optimization_method: str | None = None,
-) -> dict[tuple[str, str], SmoothedGridOptimumResult]:
-    """Pooled grid solve per (model, layer) from a grid export CSV frame."""
-    cfg = grid_solver_config(
-        optimization_config,
-        grid_cluster_count_reward=grid_cluster_count_reward,
-        grid_n_entities=grid_n_entities,
-        grid_objective=grid_objective,
-        optimization_method=optimization_method,
-    )
-    return {
-        combo: pooled_grid_solve_from_metrics_dfs(metrics_list, cfg)
-        for combo, metrics_list in per_combo_metrics_from_grid(df_grid).items()
-    }
-
-
-def resolve_chosen_min_cluster_size_by_combo_from_grid(
-    df_grid: pd.DataFrame,
-    optimization_config: ClusteringOptimizationConfig | None = None,
-    *,
-    grid_cluster_count_reward: float | None = None,
-    grid_n_entities: int | None = None,
-    grid_objective: GridObjectiveSpec | None = None,
-    optimization_method: str | None = None,
-) -> dict[tuple[str, str], int]:
-    """Re-solve ``chosen_min_cluster_size`` per (model, layer) from a grid export CSV frame."""
-    solved = solve_pooled_grid_by_combo_from_grid(
-        df_grid,
-        optimization_config,
-        grid_cluster_count_reward=grid_cluster_count_reward,
-        grid_n_entities=grid_n_entities,
-        grid_objective=grid_objective,
-        optimization_method=optimization_method,
-    )
-    return {combo: result.chosen_min_cluster_size for combo, result in solved.items()}
-
-
-def split_by_negative_label(
-    dfr: pd.DataFrame,
-    negative_label: str,
-) -> tuple[np.ndarray, pd.DataFrame]:
-    """
-    Split a mention frame into a boolean mask of synthetic-negative rows and the
-    manifold frame (KB / non-negative rows only).
-    """
-    neg_mask = dfr["entity"].astype(str).values == negative_label
-    manifold_df = dfr.loc[~neg_mask].copy()
-    return neg_mask, manifold_df
-
-
-def _pick_oov_winner_by_cv_folds(
-    fold_pairs: list[tuple[np.ndarray, np.ndarray]],
-    Xm: np.ndarray,
-    y_i: np.ndarray,
-    oov_cfg: ManifoldOovScreenerConfig,
-) -> tuple[bool, ManifoldOovKind | str]:
-    svm_oov_fold: list[float] = []
-    lda_oov_fold: list[float] = []
-    rbf_oov_fold: list[float] = []
-
-    for train_idx, test_idx in fold_pairs:
-        Xtr_m, Xtst_m = Xm[train_idx], Xm[test_idx]
-        ytr_m, ytst_m = y_i[train_idx], y_i[test_idx]
-        if len(np.unique(ytst_m)) < 2:
-            continue
-        xm64 = Xtr_m.astype(np.float64, copy=False)
-        xt64 = Xtst_m.astype(np.float64, copy=False)
-
-        lin_o = make_manifold_linear_svc(xm64, oov_cfg)
-        lin_o.fit(xm64, ytr_m)
-        svm_oov_fold.append(
-            float(
-                f1_score(
-                    ytst_m,
-                    lin_o.predict(xt64),
-                    pos_label=1,
-                    zero_division=0,
-                )
-            )
-        )
-
-        rbf_o = make_manifold_rbf_svc(oov_cfg)
-        rbf_o.fit(xm64, ytr_m)
-        rbf_oov_fold.append(
-            float(
-                f1_score(
-                    ytst_m,
-                    rbf_o.predict(xt64),
-                    pos_label=1,
-                    zero_division=0,
-                )
-            )
-        )
-
-        lda_est_o = LinearDiscriminantAnalysis(solver="svd")
-        lda_est_o.fit(xm64, ytr_m)
-        lda_oov_fold.append(
-            float(
-                f1_score(
-                    ytst_m,
-                    lda_est_o.predict(xt64),
-                    pos_label=1,
-                    zero_division=0,
-                )
-            )
-        )
-
-    if not lda_oov_fold:
-        return False, "lda"
-    svm_m_o = float(np.mean(svm_oov_fold))
-    lda_m_o = float(np.mean(lda_oov_fold))
-    rbf_m_o = float(np.mean(rbf_oov_fold))
-    return True, pick_projection_winner_by_mean_f1(lda_m_o, svm_m_o, rbf_m_o)
-
-
-def _collect_embedding_cv_folds(
-    fold_pairs: list[tuple[np.ndarray, np.ndarray]],
-    Xe: np.ndarray,
-    y_i: np.ndarray,
-    rs_emb: int,
-) -> (
-    tuple[
-        list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
-        str,
-    ]
-    | None
-):
-    embed_fold_rows: list[
-        tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
-    ] = []
-    pool_y_pooled: list[int] = []
-    pool_lda_pooled: list[float] = []
-    pool_svm_pooled: list[float] = []
-    for train_idx, test_idx in fold_pairs:
-        Xtr_e, Xtst_e = Xe[train_idx], Xe[test_idx]
-        ytr_e, ytst_e = y_i[train_idx], y_i[test_idx]
-        if len(np.unique(ytst_e)) < 2:
-            continue
-
-        lda_emb = LinearDiscriminantAnalysis(solver="svd")
-        lda_emb.fit(Xtr_e, ytr_e)
-        sc_lda = np.asarray(lda_emb.transform(Xtst_e), dtype=np.float64).ravel()
-        svm_emb = _linear_svc_for_embeddings(Xtr_e, random_state=rs_emb)
-        svm_emb.fit(Xtr_e, ytr_e)
-        sc_svm = np.asarray(
-            svm_emb.decision_function(Xtst_e),
-            dtype=np.float64,
-        ).ravel()
-        embed_fold_rows.append((train_idx, test_idx, ytst_e, sc_lda, sc_svm))
-        for j in range(int(ytst_e.shape[0])):
-            pool_y_pooled.append(int(ytst_e[j]))
-            pool_lda_pooled.append(float(sc_lda[j]))
-            pool_svm_pooled.append(float(sc_svm[j]))
-
-    if not pool_y_pooled:
-        return None
-
-    y_p = np.asarray(pool_y_pooled, dtype=np.int64)
-    s_ld_p = np.asarray(pool_lda_pooled, dtype=np.float64)
-    s_sv_p = np.asarray(pool_svm_pooled, dtype=np.float64)
-    auc_lda_g = float(roc_auc_score(y_p, s_ld_p))
-    auc_svm_g = float(roc_auc_score(y_p, s_sv_p))
-    screener_best_kind = "lda" if auc_lda_g >= auc_svm_g else "svm"
-    return embed_fold_rows, screener_best_kind
-
-
-def _oov_scores_for_fold(
-    winner_kind_oov: ManifoldOovKind | str,
-    Xm: np.ndarray,
-    train_idx: np.ndarray,
-    test_idx: np.ndarray,
-    y_i: np.ndarray,
-    oov_cfg: ManifoldOovScreenerConfig,
-) -> np.ndarray:
-    Xtr_m, Xtst_m = Xm[train_idx], Xm[test_idx]
-    ytr_e = y_i[train_idx]
-    xm_tr = Xtr_m.astype(np.float64, copy=False)
-    xm_ts = Xtst_m.astype(np.float64, copy=False)
-    if winner_kind_oov == "svm":
-        svm_w = make_manifold_linear_svc(xm_tr, oov_cfg)
-        svm_w.fit(xm_tr, ytr_e)
-        return oov_estimator_scores(svm_w, xm_ts)
-    if winner_kind_oov == "rbf":
-        rbf_w = make_manifold_rbf_svc(oov_cfg)
-        rbf_w.fit(xm_tr, ytr_e)
-        return oov_estimator_scores(rbf_w, xm_ts)
-    lda_w = LinearDiscriminantAnalysis(solver="svd")
-    lda_w.fit(xm_tr, ytr_e)
-    return oov_estimator_scores(lda_w, xm_ts)
-
-
-def _score_folds_with_oov_and_pool(
-    embed_fold_rows: list[
-        tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
-    ],
-    *,
-    screener_best_kind: str,
-    oov_disabled: bool,
-    winner_kind_oov: ManifoldOovKind | str,
-    Xm: np.ndarray | None,
-    y_i: np.ndarray,
-    entity: np.ndarray,
-    orig_idx: np.ndarray,
-    oov_cfg: ManifoldOovScreenerConfig,
-) -> (
-    tuple[
-        list[int],
-        list[str],
-        list[int],
-        list[float],
-        list[float],
-        list[float],
-        list[float],
-        list[float],
-        list[list[float]],
-    ]
-    | None
-):
-    pool_orig: list[int] = []
-    pool_ent: list[str] = []
-    pool_y: list[int] = []
-    pool_lda_s: list[float] = []
-    pool_svm_s: list[float] = []
-    pool_sb: list[float] = []
-    pool_oov: list[float] = []
-    pool_comb: list[float] = []
-
-    lda_p, lda_r, lda_f1, lda_auc_l = [], [], [], []
-    svm_p, svm_r, svm_f1, svm_auc_l = [], [], [], []
-    sb_p, sb_r, sb_f1, sb_a = [], [], [], []
-    oov_p, oov_r, oov_f1, oov_a = [], [], [], []
-    cb_p, cb_r, cb_f1, cb_a = [], [], [], []
-
-    for train_idx, test_idx, ytst_e, sc_lda, sc_svm in embed_fold_rows:
-        sb_scores = sc_lda if screener_best_kind == "lda" else sc_svm
-
-        if not oov_disabled and Xm is not None:
-            o_scores = _oov_scores_for_fold(
-                winner_kind_oov, Xm, train_idx, test_idx, y_i, oov_cfg
-            )
-            mn_s = _minmax01_fold(sb_scores)
-            mn_o = _minmax01_fold(o_scores)
-            comb_scores = 0.5 * mn_s + 0.5 * mn_o
-        else:
-            o_scores = np.zeros_like(sb_scores, dtype=np.float64)
-            comb_scores = sb_scores.astype(np.float64, copy=False)
-
-        oi_fold = np.asarray(orig_idx[test_idx], dtype=np.int64).ravel()
-        ent_fold = entity[test_idx]
-        yt = ytst_e.astype(np.int64, copy=False)
-
-        for j in range(int(yt.shape[0])):
-            pool_orig.append(int(oi_fold[j]))
-            pool_ent.append(str(ent_fold[j]))
-            pool_y.append(int(yt[j]))
-            pool_lda_s.append(float(sc_lda[j]))
-            pool_svm_s.append(float(sc_svm[j]))
-            pool_sb.append(float(sb_scores[j]))
-            pool_oov.append(float(o_scores[j]))
-            pool_comb.append(float(comb_scores[j]))
-
-        p0, r0, f00, a0 = _fold_prfa(ytst_e, sc_lda)
-        lda_p.append(p0)
-        lda_r.append(r0)
-        lda_f1.append(f00)
-        lda_auc_l.append(a0)
-        p1, r1, f01, a1 = _fold_prfa(ytst_e, sc_svm)
-        svm_p.append(p1)
-        svm_r.append(r1)
-        svm_f1.append(f01)
-        svm_auc_l.append(a1)
-        p2, r2, f02, a2 = _fold_prfa(ytst_e, sb_scores)
-        sb_p.append(p2)
-        sb_r.append(r2)
-        sb_f1.append(f02)
-        sb_a.append(a2)
-        p3, r3, f03, a3 = _fold_prfa(ytst_e, o_scores)
-        oov_p.append(p3)
-        oov_r.append(r3)
-        oov_f1.append(f03)
-        oov_a.append(a3)
-        p4, r4, f04, a4 = _fold_prfa(ytst_e, comb_scores)
-        cb_p.append(p4)
-        cb_r.append(r4)
-        cb_f1.append(f04)
-        cb_a.append(a4)
-
-    if not lda_p:
-        return None
-
-    fold_lists = [
-        lda_p,
-        lda_r,
-        lda_f1,
-        lda_auc_l,
-        svm_p,
-        svm_r,
-        svm_f1,
-        svm_auc_l,
-        sb_p,
-        sb_r,
-        sb_f1,
-        sb_a,
-        oov_p,
-        oov_r,
-        oov_f1,
-        oov_a,
-        cb_p,
-        cb_r,
-        cb_f1,
-        cb_a,
-    ]
-    return (
-        pool_orig,
-        pool_ent,
-        pool_y,
-        pool_lda_s,
-        pool_svm_s,
-        pool_sb,
-        pool_oov,
-        pool_comb,
-        fold_lists,
-    )
-
-
-def _build_all_screener_cv_result(
-    fold_lists: list[list[float]],
-    *,
-    screener_best_kind: str,
-    oov_kind_disp: str,
-    oov_disabled: bool,
-) -> AllScreenerCvResult:
-    (
-        lda_p,
-        lda_r,
-        lda_f1,
-        lda_auc_l,
-        svm_p,
-        svm_r,
-        svm_f1,
-        svm_auc_l,
-        sb_p,
-        sb_r,
-        sb_f1,
-        sb_a,
-        oov_p,
-        oov_r,
-        oov_f1,
-        oov_a,
-        cb_p,
-        cb_r,
-        cb_f1,
-        cb_a,
-    ) = fold_lists
-    lda_mets = _metrics_from_fold_lists(lda_p, lda_r, lda_f1, lda_auc_l)
-    svm_mets = _metrics_from_fold_lists(svm_p, svm_r, svm_f1, svm_auc_l)
-    sb_mets = _metrics_from_fold_lists(sb_p, sb_r, sb_f1, sb_a)
-    if oov_disabled:
-        oov_mets = _zero_binary_metrics()
-        comb_mets = sb_mets
-    else:
-        oov_mets = _metrics_from_fold_lists(oov_p, oov_r, oov_f1, oov_a)
-        comb_mets = _metrics_from_fold_lists(cb_p, cb_r, cb_f1, cb_a)
-    return AllScreenerCvResult(
-        screener_lda=lda_mets,
-        screener_svm=svm_mets,
-        screener_best_kind=screener_best_kind,
-        screener_best=sb_mets,
-        oov_winner_kind=oov_kind_disp,
-        oov=oov_mets,
-        combined=comb_mets,
-    )
-
-
-def _unified_cv_fold_count(y: np.ndarray, n_splits_requested: int) -> int | None:
-    n0 = int(np.sum(y == 0))
-    n1 = int(np.sum(y == 1))
-    if n0 < 2 or n1 < 2:
-        return None
-    max_splits = min(n0, n1)
-    n_eff = min(int(n_splits_requested), max_splits)
-    return n_eff if n_eff >= 2 else None
-
-
-def _minmax01_fold(x: np.ndarray) -> np.ndarray:
-    xf = np.asarray(x, dtype=np.float64).ravel()
-    lo = float(np.min(xf))
-    hi = float(np.max(xf))
-    if hi <= lo:
-        return np.full(xf.shape[0], 0.5, dtype=np.float64)
-    return (xf - lo) / (hi - lo)
-
-
-def _fold_prfa(
-    y_true: np.ndarray, scores: np.ndarray
-) -> tuple[float, float, float, float]:
-    y_i = np.asarray(y_true, dtype=np.int64).ravel()
-    s = np.asarray(scores, dtype=np.float64).ravel()
-    y_pred = (s > 0.0).astype(np.int64)
-    prec = float(precision_score(y_i, y_pred, pos_label=1, zero_division=0))
-    rec = float(recall_score(y_i, y_pred, pos_label=1, zero_division=0))
-    f1 = float(f1_score(y_i, y_pred, pos_label=1, zero_division=0))
-    try:
-        auc_v = float(roc_auc_score(y_i, s))
-    except ValueError:
-        auc_v = 0.5
-    if math.isnan(auc_v) or math.isinf(auc_v):
-        auc_v = 0.5
-    return prec, rec, f1, auc_v
-
-
-def _metrics_from_fold_lists(
-    precs: list[float],
-    recalls: list[float],
-    f1s: list[float],
-    aucs: list[float],
-) -> BinaryClassifierMetrics:
-    def _one(vals: list[float]) -> MetricMeanStd:
-        arr = np.asarray(vals, dtype=np.float64)
-        return MetricMeanStd(
-            mean=float(np.mean(arr)),
-            std=float(np.std(arr, ddof=1)) if arr.size > 1 else 0.0,
-        )
-
-    return BinaryClassifierMetrics(
-        precision=_one(precs),
-        recall=_one(recalls),
-        f1=_one(f1s),
-        auc=_one(aucs),
-    )
-
-
-def _zero_binary_metrics() -> BinaryClassifierMetrics:
-    z = MetricMeanStd(0.0, 0.0)
-    return BinaryClassifierMetrics(
-        precision=z, recall=z, f1=z, auc=MetricMeanStd(0.5, 0.0)
-    )
-
-
-def evaluate_all_screeners_cv(
-    X_embed: np.ndarray,
-    X_manifold: np.ndarray | None,
-    y: np.ndarray,
-    entity: np.ndarray,
-    orig_idx: np.ndarray,
-    screener_cfg: NegativeScreenerConfig,
-    oov_cfg: ManifoldOovScreenerConfig,
-) -> tuple[AllScreenerCvResult, PerDatapointScores] | None:
-    """
-    Shared-stratified-fold CV for LDA/SVM negative screener, manifold OOV model, and stacked score.
-
-    ``screener_best`` scores use the ROC winner (LDA vs SVM) on pooled OOS predictions.
-
-    When ``oov_cfg.enabled`` is False or ``X_manifold`` is None, OOV branch is skipped:
-    ``combined`` metrics match ``screener_best`` and ``oov_winner_kind`` is ``"disabled"``.
-    """
-    y_i = np.asarray(y, dtype=np.int64).ravel()
-    n_splits = _unified_cv_fold_count(y_i, screener_cfg.cv_n_splits)
-    if n_splits is None:
-        return None
-
-    splitter = StratifiedKFold(
-        n_splits=n_splits,
-        shuffle=True,
-        random_state=screener_cfg.cv_random_state,
-    )
-    Xe = np.asarray(X_embed, dtype=np.float64)
-    fold_pairs = list(splitter.split(Xe, y_i))
-    rs_emb = screener_cfg.cv_random_state
-
-    oov_run = bool(oov_cfg.enabled) and X_manifold is not None
-    Xm: np.ndarray | None = (
-        np.asarray(X_manifold, dtype=np.float64) if oov_run else None
-    )
-    winner_kind_oov: ManifoldOovKind | str = "lda"
-
-    if oov_run and Xm is not None:
-        oov_run, winner_kind_oov = _pick_oov_winner_by_cv_folds(
-            fold_pairs, Xm, y_i, oov_cfg
-        )
-        if not oov_run:
-            Xm = None
-
-    collected = _collect_embedding_cv_folds(fold_pairs, Xe, y_i, rs_emb)
-    if collected is None:
-        return None
-    embed_fold_rows, screener_best_kind = collected
-
-    oov_disabled = not oov_run or Xm is None
-    oov_kind_disp = "disabled" if oov_disabled else str(winner_kind_oov)
-
-    scored = _score_folds_with_oov_and_pool(
-        embed_fold_rows,
-        screener_best_kind=screener_best_kind,
-        oov_disabled=oov_disabled,
-        winner_kind_oov=winner_kind_oov,
-        Xm=Xm,
-        y_i=y_i,
-        entity=entity,
-        orig_idx=orig_idx,
-        oov_cfg=oov_cfg,
-    )
-    if scored is None:
-        return None
-    (
-        pool_orig,
-        pool_ent,
-        pool_y,
-        pool_lda_s,
-        pool_svm_s,
-        pool_sb,
-        pool_oov,
-        pool_comb,
-        fold_lists,
-    ) = scored
-
-    result = _build_all_screener_cv_result(
-        fold_lists,
-        screener_best_kind=screener_best_kind,
-        oov_kind_disp=oov_kind_disp,
-        oov_disabled=oov_disabled,
-    )
-
-    datapoints = PerDatapointScores(
-        orig_idx=list(pool_orig),
-        entity=list(pool_ent),
-        y_true=list(pool_y),
-        screener_lda_score=list(pool_lda_s),
-        screener_svm_score=list(pool_svm_s),
-        screener_best_score=list(pool_sb),
-        oov_score=list(pool_oov),
-        combined_score=list(pool_comb),
-    )
-    return result, datapoints
-
-
-def fit_ambient_screener_with_metrics(
-    dfr: pd.DataFrame,
-    config: NegativeScreenerConfig,
-) -> tuple[NegativeClassScreener, NegativeScreenerInSampleMetrics | None]:
-    """
-    Fit the persisted screener on ``dfr`` and report in-sample PR/F1 for detecting
-    ``negative_label`` when both classes are present.
-    """
-    screener = NegativeClassScreener.fit_from_frame(dfr, config)
-    y_true = (dfr["entity"].astype(str).values == config.negative_label).astype(
-        np.int64
-    )
-    n_kb = int(np.sum(y_true == 0))
-    n_neg = int(np.sum(y_true == 1))
-    if n_kb == 0 or n_neg == 0:
-        return screener, None
-    X = np.stack(dfr["embed"].values).astype(np.float32, copy=False)
-    y_pred = screener.predict_is_negative(X).astype(np.int64)
-    prec = float(precision_score(y_true, y_pred, pos_label=1, zero_division=0))
-    rec = float(recall_score(y_true, y_pred, pos_label=1, zero_division=0))
-    f1 = float(f1_score(y_true, y_pred, pos_label=1, zero_division=0))
-    return screener, NegativeScreenerInSampleMetrics(
-        precision=prec,
-        recall=rec,
-        f1=f1,
-        n_kb_mentions=n_kb,
-        n_negative_label_mentions=n_neg,
-        kind=config.kind,
-    )
-
-
-def compute_clustering_fit_metrics(
-    clusterer: object,
-    manifold_df: pd.DataFrame,
-    *,
-    min_cluster_size: int,
-    cluster_labels: np.ndarray,
-) -> ClusteringFitMetrics:
-    """DBCV, ARI vs ``entity``, and cluster counts for a fitted HDBSCAN model."""
-    labels = np.asarray(cluster_labels, dtype=np.int64).ravel()
-    n = int(labels.shape[0])
-    label_set = set(labels.tolist())
-    n_clusters_emergent = len(label_set) - (1 if -1 in label_set else 0)
-    noise_count = int(np.sum(labels == -1))
-    noise_fraction = float(noise_count) / float(n) if n > 0 else 0.0
-
-    rv = getattr(clusterer, "relative_validity_", None)
-    dbcv: float | None
-    if rv is None:
-        dbcv = None
-    else:
-        rv_f = float(rv)
-        if math.isnan(rv_f) or math.isinf(rv_f):
-            dbcv = None
-        else:
-            dbcv = rv_f
-
-    ari_score: float | None
-    if "entity" in manifold_df.columns and len(manifold_df) == n:
-        property_labels = manifold_df["entity"].astype("category").cat.codes.values
-        ari_score = compute_adjusted_rand_index(property_labels, labels)
-    else:
-        ari_score = None
-
-    return ClusteringFitMetrics(
-        min_cluster_size=min_cluster_size,
-        dbcv=dbcv,
-        ari=ari_score,
-        n_clusters_emergent=n_clusters_emergent,
-        noise_fraction=noise_fraction,
-        n_samples=n,
-    )
-
-
-def drop_entities_with_few_mentions(
-    frame: pd.DataFrame,
-    min_mentions_per_entity: int,
-    *,
-    negative_label: str | None = None,
-) -> pd.DataFrame:
-    """
-    Drop entities with fewer than ``min_mentions_per_entity`` rows (same rule as
-    :func:`~pelinker.selection.load_selection_frame` / mention-level selection eval).
-
-    When ``negative_label`` is set, that label is never dropped for low mention count
-    (so thin negative tails remain for screener training).
-    """
-    if "entity" not in frame.columns:
-        raise ValueError("frame must contain an 'entity' column")
-    mention_count = frame["entity"].value_counts()
-    low_count = mention_count[
-        ~(mention_count >= min_mentions_per_entity)
-    ].index.to_list()
-    if negative_label is not None:
-        low_count = [e for e in low_count if e != negative_label]
-    return frame.loc[~frame["entity"].isin(low_count)].copy()
-
-
-def embeddings_dict_to_dataframe(
-    embeddings_dict: dict[str, tuple[str, torch.Tensor | np.ndarray]],
-) -> pd.DataFrame:
-    """
-    Convert embeddings dictionary to DataFrame format expected by transform artifacts.
-
-    Args:
-        embeddings_dict: Dictionary mapping id -> (label, embedding)
-
-    Returns:
-        DataFrame with columns: id, label, embed
-    """
-    embeddings_list = []
-    id_list = []
-    label_list = []
-
-    for id_val, (label, emb) in embeddings_dict.items():
-        if isinstance(emb, torch.Tensor):
-            emb_np = emb.detach().cpu().numpy()
-        else:
-            emb_np = np.array(emb)
-        embeddings_list.append(emb_np)
-        id_list.append(id_val)
-        label_list.append(label)
-
-    return pd.DataFrame({"id": id_list, "label": label_list, "embed": embeddings_list})
-
-
-def compute_kb_generality_scores(
-    embeddings: np.ndarray,
-    labels: list[str],
-    k_neighbors: int = 10,
-    metric: str = "cosine",
-    word_frequencies: Mapping[str, float] | None = None,
-    density_weight: float = 0.5,
-) -> np.ndarray:
-    """
-    Compute generality scores for entities based on KB statistics.
-
-    Combines embedding-space density with label simplicity to identify generic vs specific terms.
-    Generic terms tend to have:
-    - Many similar neighbors (high density)
-    - High average similarity to neighbors
-    - Shorter, simpler labels (fewer words, common words)
-    - Central position in semantic space
-
-    Args:
-        embeddings: Array of shape (n_points, n_features) containing embeddings
-        labels: List of labels corresponding to embeddings
-        k_neighbors: Number of nearest neighbors to consider
-        metric: Distance metric ('cosine' or 'euclidean')
-        word_frequencies: Optional word frequency mapping for simplicity scoring
-        density_weight: Weight for embedding density vs label simplicity (0.0 = pure simplicity, 1.0 = pure density)
-
-    Returns:
-        Array of generality scores (higher = more generic), shape (n_points,)
-    """
-    from sklearn.neighbors import NearestNeighbors
-
-    n_points = embeddings.shape[0]
-    k_neighbors = min(k_neighbors, n_points - 1)
-
-    if k_neighbors < 1:
-        return np.ones(n_points)
-
-    # Normalize embeddings for cosine distance
-    if metric == "cosine":
-        embeddings_norm = embeddings / (
-            np.linalg.norm(embeddings, axis=1, keepdims=True) + 1e-8
-        )
-    else:
-        embeddings_norm = embeddings
-
-    # Find k nearest neighbors for each point
-    nn = NearestNeighbors(n_neighbors=k_neighbors + 1, metric=metric)
-    nn.fit(embeddings_norm)
-    distances, indices = nn.kneighbors(embeddings_norm)
-
-    # Compute embedding-space density scores
-    density_scores = np.zeros(n_points)
-
-    for i in range(n_points):
-        # Get neighbors (excluding self)
-        neighbor_distances = distances[i, 1:]
-
-        # Convert distances to similarities (for cosine: similarity = 1 - distance)
-        if metric == "cosine":
-            similarities = 1.0 - neighbor_distances
-        else:
-            # For euclidean, use inverse distance (with smoothing)
-            similarities = 1.0 / (1.0 + neighbor_distances)
-
-        # Density = average similarity to neighbors
-        # Higher similarity means the term is in a dense region (more generic)
-        density_scores[i] = similarities.mean()
-
-    density_scores = np.log(density_scores)
-    # Normalize density scores to [0, 1] range
-    if density_scores.max() > density_scores.min():
-        density_scores_norm = (density_scores - density_scores.min()) / (
-            density_scores.max() - density_scores.min()
-        )
-    else:
-        density_scores_norm = np.ones_like(density_scores)
-
-    # Compute label simplicity scores
-    if word_frequencies is None:
-        word_frequencies = {}
-
-    simplicity_scores = np.zeros(n_points)
-    for i, label in enumerate(labels):
-        simplicity_metrics = _measure_label_simplicity(
-            str(label), word_frequencies=word_frequencies
-        )
-        simplicity_scores[i] = simplicity_metrics["simplicity_score"]
-
-    simplicity_scores = np.log(simplicity_scores)
-
-    # Normalize simplicity scores to [0, 1] range
-    if simplicity_scores.max() > simplicity_scores.min():
-        simplicity_scores_norm = (simplicity_scores - simplicity_scores.min()) / (
-            simplicity_scores.max() - simplicity_scores.min()
-        )
-    else:
-        simplicity_scores_norm = np.ones_like(simplicity_scores)
-
-    # Combine density and simplicity scores
-    # Shorter, simpler terms should be preferred even if density is similar
-    generality_scores = (
-        density_weight * density_scores_norm
-        + (1 - density_weight) * simplicity_scores_norm
-    )
-
-    return generality_scores
diff --git a/pelinker/cli/dim_selection.py b/pelinker/cli/dim_selection.py
index 3e77e19..deb4ce2 100644
--- a/pelinker/cli/dim_selection.py
+++ b/pelinker/cli/dim_selection.py
@@ -6,22 +6,28 @@
 
 import click
 
-from pelinker.dim_selection import run_dim_selection
-from pelinker.dim_selection.checkpoint import DEFAULT_CHECKPOINT_NAME
-from pelinker.dim_selection.grids import DEFAULT_PCA_GRID, DEFAULT_UMAP_GRID
-from pelinker.onto import NEGATIVE_LABEL
+from pelinker.search.dim_selection import run_dim_selection
+from pelinker.search.dim_selection.checkpoint import DEFAULT_CHECKPOINT_NAME
+from pelinker.search.dim_selection.grids import DEFAULT_PCA_GRID, DEFAULT_UMAP_GRID
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.core.paths import ExpandedPath
+from pelinker.kb.classes import check_class_view
 
 _EPILOG = """
 MCS = min_cluster_size (HDBSCAN hyperparameter on the inner grid).
 
 Metrics (same two-level stack as model selection):
 
-  Inner (choose MCS): grid_objective=dbcv_ari_mean_minmax —
-    min-max normalize mean DBCV and mean ARI on the MCS curve, average,
-    smooth, and pick the left plateau.
+  Inner (choose MCS): grid_objective=dbcv_ari_geomean —
+    clip mean DBCV and mean ARI at 0, take sqrt(dbcv*ari) per bootstrap
+    sample, smooth, then pick the largest MCS within one *paired*
+    standard error of the best (grid_one_se_k, default 1.0).
+    The geometric mean's ranking is invariant to the scales of DBCV and
+    ARI, so neither metric has to be normalized against the curve.
 
   Outer (rank pca × umap cells): at each cell's pooled MCS, combine mean
-    DBCV and mean ARI with the same DBCV+ARI pooling (minmax across cells).
+    DBCV and mean ARI with the *same* clipped geometric mean. Each cell is
+    scored from its own numbers, independently of the other cells.
     best_score stays mean DBCV for heatmaps; outer_score chooses the winner.
 """
 
@@ -32,13 +38,13 @@
 )
 @click.option(
     "--input-parquet",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help="Single mention-level embedding parquet (one model/layer).",
 )
 @click.option(
     "--report-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help="Directory for dim-selection outputs and checkpoint.",
 )
@@ -148,6 +154,23 @@
     default=None,
     help="Seed for per-entity mention cap draws (default: --seed).",
 )
+@click.option(
+    "--class-view",
+    type=click.Choice(["raw", "rel", "reldir"]),
+    default="reldir",
+    show_default=True,
+    help=(
+        "Classes the objective's ARI scores clusters against: raw matched label, "
+        "canonical relation, or canonical relation + direction. rel/reldir need "
+        "--class-kb-path."
+    ),
+)
+@click.option(
+    "--class-kb-path",
+    type=ExpandedPath(path_type=pathlib.Path, dir_okay=False, exists=True),
+    default=None,
+    help="Pairs KB (*.pairs.csv) the mentions were embedded with; required unless --class-view raw.",
+)
 @click.option(
     "--batch-size",
     type=click.INT,
@@ -181,7 +204,7 @@
 )
 @click.option(
     "--selected-labels-kb-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     default=None,
     help="Optional path to selected labels KB CSV. If provided, clustering uses only those labels.",
 )
@@ -219,7 +242,7 @@
 )
 @click.option(
     "--checkpoint-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     default=None,
     help=f"Checkpoint JSON path (default: <report-path>/{DEFAULT_CHECKPOINT_NAME})",
 )
@@ -237,6 +260,16 @@
     show_default=True,
     help="Estimator saved on Linker when fitting from this pipeline (analysis always logs both).",
 )
+@click.option(
+    "--persist-labels/--no-persist-labels",
+    default=False,
+    show_default=True,
+    help=(
+        "Write per-bootstrap cluster assignments to sample_cluster_labels.parquet, so "
+        "cluster-identity stability can be measured across draws (run/analysis/"
+        "cluster_stability.py). Off by default: it adds one row per mention per draw."
+    ),
+)
 def main(
     input_parquet: pathlib.Path,
     report_path: pathlib.Path,
@@ -256,6 +289,8 @@ def main(
     max_mentions_per_entity: int | None,
     max_mentions_negative: int | None,
     mention_cap_seed: int | None,
+    class_view: str,
+    class_kb_path: pathlib.Path | None,
     batch_size: int,
     n_sample: int,
     prefix: str,
@@ -269,6 +304,7 @@ def main(
     checkpoint_path: pathlib.Path | None,
     negative_label: str,
     screener_kind: str,
+    persist_labels: bool,
 ) -> None:
     run_dim_selection(
         input_parquet=input_parquet,
@@ -289,6 +325,8 @@ def main(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=seed if mention_cap_seed is None else mention_cap_seed,
+        class_view=check_class_view(class_view, class_kb_path),
+        class_kb_path=class_kb_path,
         batch_size=batch_size,
         n_sample=n_sample,
         prefix=prefix,
@@ -302,6 +340,7 @@ def main(
         checkpoint_path=checkpoint_path,
         negative_label=negative_label,
         screener_kind=screener_kind,
+        persist_labels=persist_labels,
     )
 
 
diff --git a/pelinker/cli/fit.py b/pelinker/cli/fit.py
index 225f59a..31cafeb 100644
--- a/pelinker/cli/fit.py
+++ b/pelinker/cli/fit.py
@@ -9,7 +9,7 @@
 from hydra.core.config_store import ConfigStore
 from omegaconf import MISSING, OmegaConf
 
-from pelinker.config import (
+from pelinker.core.config import (
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
     EmbeddingTrainingConfig,
@@ -20,26 +20,29 @@
     NegativeScreenerConfig,
     TransformConfig,
 )
-from pelinker.embedder import embed_kb_corpus
+from pelinker.embed.corpus import embed_kb_corpus
 from pelinker.model import Linker
-from pelinker.cluster_composition_viz import (
+from pelinker.clustering.composition import (
     DEFAULT_MAX_CLUSTERS_FOR_PLOTS,
     build_cluster_composition_df,
     cluster_entity_mass_summary,
     cluster_score_percentile_summary,
     with_noise_cluster_label,
 )
-from pelinker.kb_out import KbOutNamingConfig, cluster_labels_from_catalog
-from pelinker.reporting import (
-    linker_fit_cluster_composition_path,
-    linker_fit_clustering_report_path,
-    linker_fit_kb_out_path,
+from pelinker.kb.classes import check_class_view
+from pelinker.kb.kb_out import KbOutNamingConfig, cluster_labels_from_catalog
+from pelinker.reports.io import (
     write_cluster_composition_json,
     write_clustering_report_json,
     write_kb_out_json,
 )
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.util import expand_config_path
+from pelinker.reports.paths import (
+    linker_fit_cluster_composition_path,
+    linker_fit_clustering_report_path,
+    linker_fit_kb_out_path,
+)
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.core.paths import expand_config_path
 
 logger = logging.getLogger(__name__)
 
@@ -85,6 +88,12 @@ class FitCliConfig:
     max_mentions_negative: int | None = None
     mention_cap_seed: int | None = None
     """Seed for per-entity mention cap; defaults to ``seed`` when omitted."""
+    class_view: str = "reldir"
+    """Classes the fit's ARI and catalog composition use (:mod:`pelinker.kb.classes`):
+    ``raw`` (matched label), ``rel`` (canonical relation) or ``reldir`` (canonical relation +
+    direction). ``rel`` / ``reldir`` read the pairs KB; ``raw`` reproduces earlier fits."""
+    class_kb_path: str | None = None
+    """Pairs KB for the class view; defaults to ``kb_path``."""
     seed: int = 13
     """Bootstrap seed for clustering subsample draws (``base_seed``); also default for mention-cap and screener draws."""
     pca_seed: int = 13
@@ -95,10 +104,12 @@ class FitCliConfig:
     """Max mention rows per clustering bootstrap draw (stratified). None = use all loaded rows."""
     clustering_sample_index: int = 0
     """Bootstrap index for clustering subsample (match model-selection ``sample_idx``)."""
-    # Stage-B HDBSCAN ``min_cluster_size`` (choose upstream, e.g. ``pelinker.model_selection``).
+    # Stage-B HDBSCAN ``min_cluster_size`` (choose upstream, e.g. ``pelinker.search.model_selection``).
     min_cluster_size: int | None = None
-    """Explicit HDBSCAN ``min_cluster_size``. Omit to resolve from ``scale_curve_path``,
-    or fall back to 20 when neither is given. An explicit value always wins."""
+    """Explicit HDBSCAN ``min_cluster_size``. Omit to resolve from ``selection_report``,
+    then ``scale_curve_path``, then :data:`~pelinker.core.scaling.DEFAULT_MIN_CLUSTER_SIZE`.
+    An explicit value always wins, and the origin is recorded in the fit report under
+    ``min_cluster_size_provenance``."""
     scale_curve_path: str | None = None
     """``scale_curve.json`` from ``pelinker-scale-curve``. When set (and
     ``min_cluster_size`` is not), ``min_cluster_size`` is extrapolated to this fit's
@@ -110,7 +121,7 @@ class FitCliConfig:
     embeddings_parquet: Any = MISSING
     input_text_table_path: str | None = None
     use_gpu: bool = False
-    nlp_model: str = "en_core_web_trf"
+    nlp_model: str = "en_core_web_lg"
     # Stage (A): text table I/O buffer rows, encoder batch size (GPU), optional cap on read passes.
     input_buffer_rows: int = 1000
     encoder_batch_size: int = 200
@@ -530,7 +541,9 @@ def _resolve_selection_hyperparameters(cfg: FitCliConfig) -> _ResolvedSelection:
     """
     selected = None
     if cfg.selection_report:
-        from pelinker.selected_hyperparameters import load_selected_hyperparameters
+        from pelinker.search.selected_hyperparameters import (
+            load_selected_hyperparameters,
+        )
 
         path = expand_config_path(cfg.selection_report)
         assert path is not None
@@ -561,6 +574,17 @@ def _resolve_selection_hyperparameters(cfg: FitCliConfig) -> _ResolvedSelection:
                 implied,
             )
 
+        if selected.effective_class_view != cfg.class_view:
+            logger.warning(
+                "Selection scored ARI against class_view=%r but this fit uses "
+                "class_view=%r. min_cluster_size=%d was chosen for a different reference "
+                "partition; re-run the search with --class-view %s to align them.",
+                selected.effective_class_view,
+                cfg.class_view,
+                selected.min_cluster_size,
+                cfg.class_view,
+            )
+
     def _pick(name: str, explicit, from_report, fallback):
         if explicit is not None:
             if from_report is not None and from_report != explicit:
@@ -605,10 +629,15 @@ def _pick(name: str, explicit, from_report, fallback):
 
 def _build_linker_fit_config(cfg: FitCliConfig) -> LinkerFitConfig:
     cap_seed = cfg.seed if cfg.mention_cap_seed is None else cfg.mention_cap_seed
+    class_kb_path = (
+        None
+        if cfg.class_view == "raw"
+        else expand_config_path(cfg.class_kb_path or cfg.kb_path)
+    )
     scale_curve = None
     if cfg.scale_curve_path:
         # Imported lazily: only fits that opt into the curve pay for the import.
-        from pelinker.scale_curve import load_scale_curve
+        from pelinker.search.scale_curve import load_scale_curve
 
         curve_path = expand_config_path(cfg.scale_curve_path)
         assert curve_path is not None
@@ -656,6 +685,8 @@ def _build_linker_fit_config(cfg: FitCliConfig) -> LinkerFitConfig:
             on_failure=cfg.distillation_on_failure,  # type: ignore[arg-type]
         ),
         scale_curve=scale_curve,
+        class_view=check_class_view(cfg.class_view, class_kb_path),
+        class_kb_path=None if class_kb_path is None else str(class_kb_path),
     )
 
 
diff --git a/pelinker/cli/link_files.py b/pelinker/cli/link_files.py
index 2fb3eeb..57b4354 100644
--- a/pelinker/cli/link_files.py
+++ b/pelinker/cli/link_files.py
@@ -11,17 +11,19 @@
 import click
 import pandas as pd
 
-from pelinker.ground_truth import (
+from pelinker.kb.ground_truth import (
     GroundTruthScore,
     GtSpan,
     score_predictions_against_ground_truth,
 )
-from pelinker.linker_kb_lemma import (
+from pelinker.kb.kb_out import kb_out_to_kb_in_map
+from pelinker.linker.kb_lemma import (
     KbLemmaValidationMetrics,
     aggregate_kb_lemma_validation,
 )
 from pelinker.model import DEFAULT_CLUSTER_MEMBERSHIP_THRESHOLD, Linker
-from pelinker.onto import MAX_LENGTH
+from pelinker.core.onto import MAX_LENGTH
+from pelinker.core.paths import ExpandedPath
 
 logger = logging.getLogger(__name__)
 
@@ -76,6 +78,7 @@ def _fmt_rate(x: float | None) -> str:
 def _score_against_ground_truth(
     out: dict[str, Any],
     ground_truth_by_doc: list[list[dict[str, Any]] | None],
+    predicted_id_to_kb_in: dict[str, str] | None = None,
 ) -> "GroundTruthScore | None":
     """Score emitted entities against the char-offset gold spans, if any are present."""
     gold: list[GtSpan] = []
@@ -100,7 +103,9 @@ def _score_against_ground_truth(
     if not gold:
         return None
     entities = out.get("entities") or []
-    return score_predictions_against_ground_truth(entities, gold)
+    return score_predictions_against_ground_truth(
+        entities, gold, predicted_id_to_kb_in=predicted_id_to_kb_in
+    )
 
 
 def _aggregate_lemma_validation(pres: object) -> "KbLemmaValidationMetrics | None":
@@ -271,7 +276,7 @@ def _flatten_inputs(
     "-m",
     "--model",
     "model_path",
-    type=click.Path(path_type=Path),
+    type=ExpandedPath(path_type=Path),
     required=True,
     help="Linker artifact path (same as Linker.dump / Linker.load, with or without .gz).",
 )
@@ -304,14 +309,14 @@ def _flatten_inputs(
     "-o",
     "--output",
     "output_path",
-    type=click.Path(path_type=Path),
+    type=ExpandedPath(path_type=Path),
     default=None,
     help="Write the entity report JSON (UTF-8) to this path.",
 )
 @click.option(
     "--dump-mention-anomaly",
     "dump_mention_anomaly",
-    type=click.Path(path_type=Path),
+    type=ExpandedPath(path_type=Path),
     default=None,
     help=(
         "If set, write one row per extracted mention with is_kb_match and PCA anomaly "
@@ -330,7 +335,7 @@ def _flatten_inputs(
     "files",
     nargs=-1,
     required=True,
-    type=click.Path(exists=True, readable=True, path_type=Path),
+    type=ExpandedPath(exists=True, readable=True, path_type=Path),
 )
 def main(
     model_path: Path,
@@ -406,7 +411,14 @@ def main(
         out["ground_truth"] = ground_truth_by_doc
         # Previously the ground truth was parsed and echoed but never scored; the README
         # pointed at a scoring script that does not exist. Score it here instead.
-        score = _score_against_ground_truth(out, ground_truth_by_doc)
+        # The KB-out catalog translates minted cluster ids back onto input-KB ids so
+        # entity accuracy is computable, not permanently undefined.
+        id_bridge = (
+            kb_out_to_kb_in_map(linker.kb_out_catalog)
+            if linker.kb_out_catalog is not None
+            else None
+        )
+        score = _score_against_ground_truth(out, ground_truth_by_doc, id_bridge)
         if score is not None:
             out["ground_truth_score"] = score.to_jsonable()
             logger.info(
@@ -422,8 +434,9 @@ def main(
             )
             if score.n_id_comparable == 0 and score.n_matched > 0:
                 logger.info(
-                    "Entity accuracy is undefined: predicted ids are minted KB-out "
-                    "cluster ids and the gold file carries input-KB ids. Detection "
+                    "Entity accuracy is undefined: no predicted id could be mapped "
+                    "onto an input-KB id (old artifact without a KB-out catalog, or "
+                    "every matched cluster is negative-dominated). Detection "
                     "precision/recall above are still meaningful."
                 )
 
diff --git a/pelinker/cli/model_selection.py b/pelinker/cli/model_selection.py
index 941140d..5cbcbda 100644
--- a/pelinker/cli/model_selection.py
+++ b/pelinker/cli/model_selection.py
@@ -6,24 +6,30 @@
 
 import click
 
-from pelinker.model_selection import run_model_selection
-from pelinker.model_selection_checkpoint import DEFAULT_CHECKPOINT_NAME, RunMode
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.reporting import MODEL_SELECTION_RUN_REPORT_BASENAME
+from pelinker.search.model_selection import run_model_selection
+from pelinker.search.model_selection_checkpoint import DEFAULT_CHECKPOINT_NAME, RunMode
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.reports.paths import MODEL_SELECTION_RUN_REPORT_BASENAME
+from pelinker.core.paths import ExpandedPath
+from pelinker.kb.classes import check_class_view
 
 _EPILOG = """
 MCS = min_cluster_size (HDBSCAN hyperparameter on the inner grid).
 
 Metrics (same two-level stack as dim selection):
 
-  Inner (choose MCS): grid_objective=dbcv_ari_mean_minmax —
-    min-max normalize mean DBCV and mean ARI on the MCS curve, average,
-    smooth, and pick the left plateau.
+  Inner (choose MCS): grid_objective=dbcv_ari_geomean —
+    clip mean DBCV and mean ARI at 0, take sqrt(dbcv*ari) per bootstrap
+    sample, smooth, then pick the largest MCS within one *paired*
+    standard error of the best (grid_one_se_k, default 1.0).
+    The geometric mean's ranking is invariant to the scales of DBCV and
+    ARI, so neither metric has to be normalized against the curve.
 
   Outer (rank model × layer): at each combo's pooled MCS, combine mean
-    DBCV and mean ARI with the same DBCV+ARI pooling (minmax across
-    candidates). best_score stays mean DBCV for heatmaps; outer_score
-    chooses the winner. Fusion proxies use resume-safe 0.5·(DBCV+ARI).
+    DBCV and mean ARI with the *same* clipped geometric mean. Each row is
+    scored from its own numbers, so adding a candidate cannot reorder the
+    others. best_score stays mean DBCV for heatmaps; outer_score chooses
+    the winner.
 """
 
 
@@ -33,13 +39,13 @@
 )
 @click.option(
     "--input-dir",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help="Directory containing parquet files",
 )
 @click.option(
     "--report-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help=(
         "Directory for all run outputs. Canonical artifact: "
@@ -144,6 +150,23 @@
     default=None,
     help="Seed for per-entity mention cap draws (default: --seed).",
 )
+@click.option(
+    "--class-view",
+    type=click.Choice(["raw", "rel", "reldir"]),
+    default="reldir",
+    show_default=True,
+    help=(
+        "Classes the objective's ARI scores clusters against: raw matched label, "
+        "canonical relation, or canonical relation + direction. rel/reldir need "
+        "--class-kb-path."
+    ),
+)
+@click.option(
+    "--class-kb-path",
+    type=ExpandedPath(path_type=pathlib.Path, dir_okay=False, exists=True),
+    default=None,
+    help="Pairs KB (*.pairs.csv) the mentions were embedded with; required unless --class-view raw.",
+)
 @click.option(
     "--batch-size",
     type=click.INT,
@@ -164,7 +187,7 @@
 )
 @click.option(
     "--selected-labels-kb-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     default=None,
     help="Optional path to selected labels KB CSV file. If provided, clustering will only use labels from this KB.",
 )
@@ -220,7 +243,7 @@
 )
 @click.option(
     "--checkpoint-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     default=None,
     help=f"Checkpoint JSON path (default: <report-path>/{DEFAULT_CHECKPOINT_NAME})",
 )
@@ -266,6 +289,8 @@ def main(
     max_mentions_per_entity: int | None,
     max_mentions_negative: int | None,
     mention_cap_seed: int | None,
+    class_view: str,
+    class_kb_path: pathlib.Path | None,
     batch_size: int,
     n_sample: int,
     prefix: str,
@@ -299,6 +324,8 @@ def main(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=seed if mention_cap_seed is None else mention_cap_seed,
+        class_view=check_class_view(class_view, class_kb_path),
+        class_kb_path=class_kb_path,
         batch_size=batch_size,
         n_sample=n_sample,
         prefix=prefix,
diff --git a/pelinker/cli/replot.py b/pelinker/cli/replot.py
index 9ae73bf..2676361 100644
--- a/pelinker/cli/replot.py
+++ b/pelinker/cli/replot.py
@@ -4,12 +4,14 @@
 
 import pathlib
 import sys
+from dataclasses import replace
 
 import click
 
-from pelinker.config import ClusteringOptimizationConfig
-from pelinker.model_selection import render_model_selection_summary_figures
-from pelinker.model_selection_checkpoint import DEFAULT_CHECKPOINT_NAME
+from pelinker.core.config import ClusteringOptimizationConfig
+from pelinker.search.model_selection import render_model_selection_summary_figures
+from pelinker.search.model_selection_checkpoint import DEFAULT_CHECKPOINT_NAME
+from pelinker.core.paths import ExpandedPath
 
 
 @click.command(
@@ -28,14 +30,14 @@
 )
 @click.argument(
     "report_dir",
-    type=click.Path(
+    type=ExpandedPath(
         path_type=pathlib.Path, exists=True, file_okay=False, dir_okay=True
     ),
 )
 @click.option(
     "--checkpoint",
     "-c",
-    type=click.Path(path_type=pathlib.Path, dir_okay=False),
+    type=ExpandedPath(path_type=pathlib.Path, dir_okay=False),
     default=None,
     help=f"Checkpoint path (default: <report-dir>/{DEFAULT_CHECKPOINT_NAME})",
 )
@@ -51,6 +53,15 @@
     default=None,
     help="Reference entity count for the cluster-count term (default: max clusters on grid).",
 )
+@click.option(
+    "--grid-one-se-k",
+    type=float,
+    default=None,
+    help=(
+        "How many paired standard errors from the best grid point still count as a tie "
+        "when re-solving chosen min_cluster_size. 0 = pure argmax (default: 1.0)."
+    ),
+)
 @click.option(
     "--all-pca-pairgrid-samples",
     is_flag=True,
@@ -65,15 +76,21 @@ def main(
     checkpoint: pathlib.Path | None,
     grid_cluster_count_reward: float | None,
     grid_n_entities: int | None,
+    grid_one_se_k: float | None,
     all_pca_pairgrid_samples: bool,
 ) -> None:
     report_dir = report_dir.expanduser().resolve()
     opt_config: ClusteringOptimizationConfig | None = None
-    if grid_cluster_count_reward is not None or grid_n_entities is not None:
-        opt_config = ClusteringOptimizationConfig(
-            grid_cluster_count_reward=grid_cluster_count_reward or 0.0,
-            grid_n_entities=grid_n_entities,
-        )
+    overrides = {
+        "grid_cluster_count_reward": grid_cluster_count_reward,
+        "grid_n_entities": grid_n_entities,
+        "grid_one_se_k": grid_one_se_k,
+    }
+    supplied = {k: v for k, v in overrides.items() if v is not None}
+    if supplied:
+        # Start from defaults and apply only what was asked for, so unrelated solver knobs
+        # keep their live-run values instead of being silently reset.
+        opt_config = replace(ClusteringOptimizationConfig(), **supplied)
     res = render_model_selection_summary_figures(
         report_dir,
         checkpoint_path=checkpoint,
diff --git a/pelinker/cli/scale_curve.py b/pelinker/cli/scale_curve.py
index 31e0bba..7532bf3 100644
--- a/pelinker/cli/scale_curve.py
+++ b/pelinker/cli/scale_curve.py
@@ -6,8 +6,10 @@
 
 import click
 
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.scale_curve import DEFAULT_RUNGS, run_scale_curve
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.search.scale_curve import DEFAULT_RUNGS, run_scale_curve
+from pelinker.core.paths import ExpandedPath
+from pelinker.kb.classes import check_class_view
 
 _EPILOG = """
 Why: min_cluster_size is an absolute row count (and, by HDBSCAN's default,
@@ -35,13 +37,13 @@
 )
 @click.option(
     "--input-parquet",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help="Single mention-level embedding parquet (one model/layer).",
 )
 @click.option(
     "--report-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help="Directory for scale_curve.json, the log-log figure, and grid rows.",
 )
@@ -131,6 +133,23 @@
     default=None,
     help="Defaults to --seed when omitted.",
 )
+@click.option(
+    "--class-view",
+    type=click.Choice(["raw", "rel", "reldir"]),
+    default="reldir",
+    show_default=True,
+    help=(
+        "Classes the objective's ARI scores clusters against: raw matched label, "
+        "canonical relation, or canonical relation + direction. rel/reldir need "
+        "--class-kb-path."
+    ),
+)
+@click.option(
+    "--class-kb-path",
+    type=ExpandedPath(path_type=pathlib.Path, dir_okay=False, exists=True),
+    default=None,
+    help="Pairs KB (*.pairs.csv) the mentions were embedded with; required unless --class-view raw.",
+)
 def main(
     input_parquet: pathlib.Path,
     report_path: pathlib.Path,
@@ -158,6 +177,8 @@ def main(
     max_mentions_per_entity: int | None,
     max_mentions_negative: int | None,
     mention_cap_seed: int | None,
+    class_view: str,
+    class_kb_path: pathlib.Path | None,
 ) -> None:
     """Measure how the chosen min_cluster_size scales with the mention-frame size."""
     curve = run_scale_curve(
@@ -187,6 +208,8 @@ def main(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=seed if mention_cap_seed is None else mention_cap_seed,
+        class_view=check_class_view(class_view, class_kb_path),
+        class_kb_path=class_kb_path,
     )
     if curve is None:
         raise SystemExit(1)
diff --git a/pelinker/cli/server.py b/pelinker/cli/server.py
index bd3e2ef..bbb3172 100644
--- a/pelinker/cli/server.py
+++ b/pelinker/cli/server.py
@@ -20,9 +20,9 @@
 from pydantic import BaseModel, ConfigDict, Field, model_validator
 from starlette.middleware.gzip import GZipMiddleware
 
-from pelinker.config import EmbeddingModelMetadata, KBConfig
+from pelinker.core.config import EmbeddingModelMetadata, KBConfig
 from pelinker.model import DEFAULT_CLUSTER_MEMBERSHIP_THRESHOLD, Linker
-from pelinker.onto import MAX_LENGTH
+from pelinker.core.onto import MAX_LENGTH
 
 logger = logging.getLogger(__name__)
 
@@ -137,7 +137,7 @@ def _transform_to_jsonable(linker: Linker) -> dict[str, Any] | None:
 def _embedding_metadata_to_json(
     em: EmbeddingModelMetadata | None,
 ) -> dict[str, Any] | None:
-    """Full serialization of :class:`~pelinker.config.EmbeddingModelMetadata` from the artifact."""
+    """Full serialization of :class:`~pelinker.core.config.EmbeddingModelMetadata` from the artifact."""
     if em is None:
         return None
     return asdict(em)
@@ -157,6 +157,9 @@ def build_info_payload(state: ServerState) -> dict[str, Any]:
         "vocabulary_size": len(linker.vocabulary),
         "cluster_count": len(cluster_ids),
         "transform_config": _transform_to_jsonable(linker),
+        # True for a linker fitted with a class view: /link rows then carry
+        # ``direction_predicted``.
+        "emits_direction": bool(linker.cluster_direction),
     }
 
 
diff --git a/run/obsolete/__init__.py b/pelinker/clustering/__init__.py
similarity index 100%
rename from run/obsolete/__init__.py
rename to pelinker/clustering/__init__.py
diff --git a/pelinker/cluster_composition_viz.py b/pelinker/clustering/composition.py
similarity index 100%
rename from pelinker/cluster_composition_viz.py
rename to pelinker/clustering/composition.py
diff --git a/pelinker/clustering_fit.py b/pelinker/clustering/fit.py
similarity index 92%
rename from pelinker/clustering_fit.py
rename to pelinker/clustering/fit.py
index 4316ed8..b7ccfa9 100644
--- a/pelinker/clustering_fit.py
+++ b/pelinker/clustering/fit.py
@@ -8,10 +8,13 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.analysis import compute_clustering_fit_metrics
-from pelinker.config import TransformConfig
-from pelinker.reporting import ClusteringFitMetrics
-from pelinker.transform import EmbeddingTransformer, score_transform_artifacts
+from pelinker.clustering.metrics import compute_clustering_fit_metrics
+from pelinker.core.config import TransformConfig
+from pelinker.reports.schema import ClusteringFitMetrics
+from pelinker.clustering.transform import (
+    EmbeddingTransformer,
+    score_transform_artifacts,
+)
 
 
 @dataclass(frozen=True, slots=True)
@@ -78,7 +81,7 @@ def fit_transformer_artifacts_on_manifold(
     manifold_df: pd.DataFrame,
     transform_config: TransformConfig,
 ):
-    """Fit transformer and return :class:`~pelinker.transform.TransformArtifacts` with UMAP."""
+    """Fit transformer and return :class:`~pelinker.clustering.transform.TransformArtifacts` with UMAP."""
     tx = fit_transformer_on_manifold(manifold_df, transform_config)
     return score_transform_artifacts(
         manifold_df,
diff --git a/pelinker/clustering_fusion_ranking.py b/pelinker/clustering/fusion_ranking.py
similarity index 100%
rename from pelinker/clustering_fusion_ranking.py
rename to pelinker/clustering/fusion_ranking.py
diff --git a/pelinker/clustering/grid.py b/pelinker/clustering/grid.py
new file mode 100644
index 0000000..a303b9b
--- /dev/null
+++ b/pelinker/clustering/grid.py
@@ -0,0 +1,875 @@
+"""HDBSCAN min_cluster_size grid evaluation, cross-sample aggregation, and smooth optimum selection."""
+
+from __future__ import annotations
+
+import logging
+import warnings
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Literal
+
+from pelinker.core.config import GridObjectiveSpec
+from pelinker.kb.classes import reference_label_column
+
+import hdbscan
+import numpy as np
+import pandas as pd
+import torch
+from sklearn.metrics import adjusted_rand_score
+from torch.nn import functional as F
+
+logger = logging.getLogger(__name__)
+
+_FLAT_CURVE_EPS = 1e-12
+"""Below this range, a pooled objective curve is treated as carrying no signal."""
+
+
+@dataclass(frozen=True)
+class ScalarMetricAggregate:
+    """Mean, dispersion, and sample count for one metric at a single grid point."""
+
+    mean: float
+    std: float
+    count: int
+
+
+@dataclass(frozen=True)
+class AggregatedGridPoint:
+    """One grid value of ``min_cluster_size`` with aggregated metrics across samples."""
+
+    min_cluster_size: int
+    dbcv: ScalarMetricAggregate
+    icm_mean: float
+    n_clusters_mean: float
+    ari: ScalarMetricAggregate
+
+
+@dataclass(frozen=True)
+class AggregatedGridReport:
+    """Typed aggregation of per-sample grid metrics; points are sorted by ``min_cluster_size``."""
+
+    points: tuple[AggregatedGridPoint, ...]
+
+    def __post_init__(self) -> None:
+        sizes = [p.min_cluster_size for p in self.points]
+        if sizes != sorted(sizes):
+            raise ValueError(
+                "AggregatedGridReport.points must be sorted by min_cluster_size"
+            )
+        if len(set(sizes)) != len(sizes):
+            raise ValueError(
+                "Duplicate min_cluster_size in AggregatedGridReport.points"
+            )
+
+
+SelectionKind = Literal[
+    # Legacy values, retained so previously written grid_chosen_hyperparameters.json stays readable.
+    "plateau_derivative",
+    "smoothed_argmax",
+    # Current selector.
+    "argmax",
+    "one_se_paired",
+    "degenerate_fallback",
+]
+
+
+@dataclass(frozen=True)
+class PairedGridScores:
+    """Per-sample objective values on a shared ``min_cluster_size`` grid.
+
+    ``scores`` is ``(n_samples, n_grid)``; entry ``[b, i]`` is the combined objective for
+    bootstrap sample ``b`` at grid point ``i``, or NaN where that sample had no usable fit.
+    Keeping the samples un-pooled is what makes *paired* comparisons across grid points
+    possible: every column was evaluated on the same samples, so differencing two columns
+    within a sample cancels the sample-level variation they share.
+    """
+
+    min_cluster_size: tuple[int, ...]
+    scores: np.ndarray
+    n_clusters_mean: tuple[float, ...]
+    objective: GridObjectiveSpec
+    n_clipped: int = 0
+    """Count of ``(sample, grid)`` cells where a negative DBCV or ARI was clipped to 0."""
+    degenerate: bool = False
+    """True when the requested combined objective carried no signal and DBCV was used instead."""
+
+    def __post_init__(self) -> None:
+        if self.scores.ndim != 2:
+            raise ValueError("PairedGridScores.scores must be 2-D (n_samples, n_grid)")
+        if self.scores.shape[1] != len(self.min_cluster_size):
+            raise ValueError("scores columns must match min_cluster_size length")
+        if len(self.n_clusters_mean) != len(self.min_cluster_size):
+            raise ValueError("n_clusters_mean must match min_cluster_size length")
+
+    @property
+    def n_samples(self) -> int:
+        return int(self.scores.shape[0])
+
+
+@dataclass(frozen=True)
+class SmoothedGridOptimumResult:
+    """Diagnostics for the ``min_cluster_size`` solvers.
+
+    ``score_mean_at_chosen`` / ``score_std_at_chosen`` refer to the raw objective (before
+    smoothing and before any cluster-count term) at the chosen grid point.
+
+    ``y_se`` holds the standard error of ``y_smooth[i] - y_smooth[argmax]`` — a *paired*
+    quantity, so it is 0 at the argmax by construction and is undefined without a reference
+    point. ``y_eligible[i]`` is True when grid point ``i`` is within ``one_se_k`` of those
+    standard errors of the best point.
+    """
+
+    chosen_min_cluster_size: int
+    score_mean_at_chosen: float
+    score_std_at_chosen: float
+    n_clusters_mean_at_chosen: float
+    x: tuple[float, ...]
+    y_objective: tuple[float, ...]
+    y_cluster_term: tuple[float, ...]
+    y_smooth: tuple[float, ...]
+    selection: SelectionKind
+    y_se: tuple[float, ...] = ()
+    y_eligible: tuple[bool, ...] = ()
+    argmax_min_cluster_size: int | None = None
+    one_se_k: float = 0.0
+    n_clipped: int = 0
+    n_samples: int = 0
+
+
+def _ensure_odd_window(window: int) -> int:
+    if window < 1:
+        raise ValueError("smooth window must be >= 1")
+    return window if window % 2 == 1 else window + 1
+
+
+def _uniform_centered_moving_average(values: np.ndarray, window: int) -> np.ndarray:
+    """Centered moving average along the last axis, ignoring NaNs within each window.
+
+    Applied per bootstrap sample rather than to the pooled curve. A moving average is
+    linear, so smoothing each sample and then averaging gives the same pooled curve as
+    smoothing the pooled curve — but it keeps the paired differences between grid points
+    consistent with the smoothed means they are compared against.
+    """
+    w = _ensure_odd_window(window)
+    half = w // 2
+    arr = np.atleast_2d(np.asarray(values, dtype=np.float64))
+    n = arr.shape[-1]
+    out = np.full(arr.shape, np.nan, dtype=np.float64)
+    for i in range(n):
+        lo = max(0, i - half)
+        hi = min(n, i + half + 1)
+        chunk = arr[:, lo:hi]
+        with warnings.catch_warnings():
+            # All-NaN windows are expected on ragged grids; they stay NaN.
+            warnings.simplefilter("ignore", RuntimeWarning)
+            out[:, i] = np.nanmean(chunk, axis=1)
+    return out.reshape(np.shape(values))
+
+
+def _metric_vectors(
+    report: AggregatedGridReport,
+    which: Literal["dbcv", "ari"],
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    if which == "dbcv":
+        means = np.array([p.dbcv.mean for p in report.points], dtype=np.float64)
+        stds = np.array([p.dbcv.std for p in report.points], dtype=np.float64)
+        counts = np.array([p.dbcv.count for p in report.points], dtype=np.float64)
+    else:
+        means = np.array([p.ari.mean for p in report.points], dtype=np.float64)
+        stds = np.array([p.ari.std for p in report.points], dtype=np.float64)
+        counts = np.array([p.ari.count for p in report.points], dtype=np.float64)
+    return means, stds, counts
+
+
+def combine_clipped_geomean(dbcv: np.ndarray, ari: np.ndarray) -> np.ndarray:
+    """Combine DBCV and ARI into one "both must be good" score on a fixed [0, 1] scale.
+
+    Both metrics already share a run-independent meaning: 0 is "no density structure" /
+    "chance agreement", 1 is perfect. Clip to that floor and take the geometric mean::
+
+        g = sqrt(max(dbcv, 0) * max(ari, 0))
+
+    Two properties matter, and neither holds for the arithmetic mean:
+
+    * **Scale invariance.** ``g(c1*dbcv, c2*ari) = sqrt(c1*c2) * g(dbcv, ari)`` — one
+      positive factor across the whole grid, so the *ranking* of grid points does not
+      change. The metrics never have to be put on a common scale, which is what made the
+      old per-curve min–max normalization necessary (and made it amplify the noise of
+      whichever metric happened to be flat).
+    * **Conjunctivity.** A high DBCV cannot compensate for a near-zero ARI. "Maximize both"
+      is a conjunction; an arithmetic mean lets one metric buy off the other.
+
+    NaN in either input propagates, marking that grid point unusable for that sample.
+    """
+    d = np.clip(np.asarray(dbcv, dtype=np.float64), 0.0, None)
+    a = np.clip(np.asarray(ari, dtype=np.float64), 0.0, None)
+    return np.sqrt(d * a)
+
+
+def geomean_std(
+    dbcv: np.ndarray,
+    dbcv_std: np.ndarray,
+    ari: np.ndarray,
+    ari_std: np.ndarray,
+) -> np.ndarray:
+    """Propagate uncertainty through :func:`combine_clipped_geomean` (delta method).
+
+    For ``g = sqrt(d*a)`` the *relative* errors add in quadrature::
+
+        se_g / g = 0.5 * sqrt((se_d/d)**2 + (se_a/a)**2)
+
+    A clipped-to-zero metric pins ``g`` at 0, so its uncertainty is reported as 0 too.
+    """
+    d = np.clip(np.asarray(dbcv, dtype=np.float64), 0.0, None)
+    a = np.clip(np.asarray(ari, dtype=np.float64), 0.0, None)
+    g = np.sqrt(d * a)
+    sd = np.nan_to_num(np.asarray(dbcv_std, dtype=np.float64), nan=0.0)
+    sa = np.nan_to_num(np.asarray(ari_std, dtype=np.float64), nan=0.0)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        rel = np.sqrt(np.square(sd / d) + np.square(sa / a))
+        out = 0.5 * g * rel
+    return np.where(np.isfinite(out), out, 0.0)
+
+
+def _combine_for_objective(
+    dbcv: np.ndarray, ari: np.ndarray, objective: GridObjectiveSpec
+) -> np.ndarray:
+    if objective == "dbcv":
+        return np.asarray(dbcv, dtype=np.float64)
+    if objective == "ari":
+        return np.asarray(ari, dtype=np.float64)
+    if objective == "dbcv_ari_geomean":
+        return combine_clipped_geomean(dbcv, ari)
+    raise ValueError(f"Unknown grid objective: {objective!r}")
+
+
+def build_paired_grid_scores(
+    metrics_dfs: Sequence[pd.DataFrame],
+    *,
+    objective: GridObjectiveSpec = "dbcv_ari_geomean",
+) -> PairedGridScores:
+    """Stack per-sample grid metrics into an ``(n_samples, n_grid)`` objective matrix.
+
+    Samples may cover different ``min_cluster_size`` values — ``evaluate_cluster_size_grid``
+    drops points that produced no clusters — so every sample is reindexed onto the *union*
+    of grid values with NaN. Intersecting instead would silently shrink the grid to whatever
+    the worst sample happened to support.
+
+    Falls back to a DBCV-only objective when no sample has any ARI (no ``entity`` column).
+    """
+    if not metrics_dfs:
+        raise ValueError("metrics_dfs must be non-empty")
+
+    grid = sorted(
+        {
+            int(v)
+            for df in metrics_dfs
+            for v in pd.to_numeric(df["min_cluster_size"], errors="coerce").dropna()
+        }
+    )
+    if not grid:
+        raise ValueError("No min_cluster_size values in metrics_dfs")
+
+    n_grid = len(grid)
+    dbcv = np.full((len(metrics_dfs), n_grid), np.nan, dtype=np.float64)
+    ari = np.full((len(metrics_dfs), n_grid), np.nan, dtype=np.float64)
+    n_clusters = np.full((len(metrics_dfs), n_grid), np.nan, dtype=np.float64)
+
+    for b, df in enumerate(metrics_dfs):
+        # Last row wins on duplicate grid values, matching the per-sample CSV dedupe.
+        indexed = df.set_index(df["min_cluster_size"].astype(int))
+        indexed = indexed[~indexed.index.duplicated(keep="last")].reindex(grid)
+        dbcv[b] = pd.to_numeric(indexed.get("dbcv"), errors="coerce").to_numpy(
+            dtype=np.float64
+        )
+        if "ari" in indexed.columns:
+            ari[b] = pd.to_numeric(indexed["ari"], errors="coerce").to_numpy(
+                dtype=np.float64
+            )
+        if "n_clusters" in indexed.columns:
+            n_clusters[b] = pd.to_numeric(
+                indexed["n_clusters"], errors="coerce"
+            ).to_numpy(dtype=np.float64)
+
+    resolved = objective
+    if objective == "dbcv_ari_geomean" and not np.any(np.isfinite(ari)):
+        logger.info(
+            "No ARI values on the min_cluster_size grid (no entity labels); "
+            "falling back to a DBCV-only grid objective."
+        )
+        resolved = "dbcv"
+
+    n_clipped = 0
+    if resolved == "dbcv_ari_geomean":
+        n_clipped = int(np.sum((dbcv < 0) | (ari < 0)))
+        if n_clipped:
+            logger.info(
+                "Clipped %d negative DBCV/ARI cell(s) to 0 before the geometric mean; "
+                "heavy clipping means the grid is largely outside the useful region.",
+                n_clipped,
+            )
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        scores = _combine_for_objective(dbcv, ari, resolved)
+        n_clusters_mean = np.nanmean(n_clusters, axis=0)
+
+        degenerate = False
+        if resolved == "dbcv_ari_geomean":
+            pooled = np.nanmean(scores, axis=0)
+            finite = pooled[np.isfinite(pooled)]
+            if (
+                finite.size
+                and float(np.max(finite) - np.min(finite)) <= _FLAT_CURVE_EPS
+            ):
+                # e.g. ARI identically 0, or DBCV <= 0 everywhere: the geometric mean is
+                # flat and carries no information about min_cluster_size. Say so loudly
+                # rather than picking an arbitrary point off a constant curve.
+                logger.warning(
+                    "Combined DBCV+ARI objective is flat across the min_cluster_size grid "
+                    "(range <= %g); falling back to DBCV alone. Check whether ARI is "
+                    "identically zero or DBCV is non-positive over the whole grid.",
+                    _FLAT_CURVE_EPS,
+                )
+                resolved = "dbcv"
+                degenerate = True
+                scores = _combine_for_objective(dbcv, ari, resolved)
+
+    return PairedGridScores(
+        min_cluster_size=tuple(grid),
+        scores=scores,
+        n_clusters_mean=tuple(float(v) for v in n_clusters_mean),
+        objective=resolved,
+        n_clipped=n_clipped,
+        degenerate=degenerate,
+    )
+
+
+def _cluster_count_reward_term(
+    n_clusters: np.ndarray,
+    *,
+    cluster_count_reward: float,
+    n_entities: int | None,
+) -> np.ndarray:
+    """Additive log penalty: 0 at ``n_ref`` clusters, negative when fewer clusters."""
+    if cluster_count_reward == 0.0:
+        return np.zeros_like(n_clusters, dtype=np.float64)
+    n_clust = np.maximum(n_clusters.astype(np.float64), 1.0)
+    n_ref = float(n_entities) if n_entities is not None else float(np.max(n_clust))
+    if n_ref <= 0:
+        return np.zeros_like(n_clust, dtype=np.float64)
+    return cluster_count_reward * np.log(n_clust / n_ref)
+
+
+def _validate_solver_args(
+    *, cluster_count_reward: float, n_entities: int | None, one_se_k: float
+) -> None:
+    if cluster_count_reward < 0:
+        raise ValueError("cluster_count_reward must be >= 0")
+    if n_entities is not None and n_entities < 1:
+        raise ValueError("n_entities must be >= 1 when provided")
+    if one_se_k < 0:
+        raise ValueError("one_se_k must be >= 0")
+
+
+def _paired_standard_errors(y: np.ndarray, i_star: int) -> np.ndarray:
+    """Standard error of ``y[:, i] - y[:, i_star]`` for every column ``i``.
+
+    Every grid point was evaluated on the *same* bootstrap samples, so comparisons across
+    ``min_cluster_size`` are paired. Differencing within each sample before taking the
+    spread removes the sample-level variation that all grid points share, which is usually
+    the dominant term. The result is much tighter — and far more stable across runs — than
+    the unpaired ``sqrt(se_i**2 + se_istar**2)``.
+
+    Zero at ``i_star`` by construction, and zero wherever fewer than two samples overlap.
+    """
+    n_grid = y.shape[1]
+    se = np.zeros(n_grid, dtype=np.float64)
+    ref = y[:, i_star]
+    for i in range(n_grid):
+        if i == i_star:
+            continue
+        delta = y[:, i] - ref
+        valid = np.isfinite(delta)
+        n_pair = int(np.count_nonzero(valid))
+        if n_pair < 2:
+            continue
+        se[i] = float(np.std(delta[valid], ddof=1) / np.sqrt(n_pair))
+    return se
+
+
+def _one_se_pick(
+    mu: np.ndarray,
+    se: np.ndarray,
+    *,
+    one_se_k: float,
+    contiguous: bool,
+) -> tuple[int, int, np.ndarray]:
+    """Apply the one-standard-error rule. Returns ``(chosen_idx, argmax_idx, eligible)``.
+
+    Uncertainty may only move the choice to a grid point that is *statistically
+    indistinguishable* from the best one — it can never promote a point with a genuinely
+    worse mean, which is what a ``mean - k*std`` discount does.
+    """
+    i_star = int(np.nanargmax(mu))
+    with np.errstate(invalid="ignore"):
+        eligible = np.isfinite(mu) & (mu >= mu[i_star] - one_se_k * se)
+    eligible[i_star] = True
+
+    chosen = i_star
+    if contiguous:
+        # Walk right through consecutive eligible points. An isolated far-right point that
+        # only qualifies because its error bar is wide is not a plateau.
+        j = i_star + 1
+        while j < len(mu) and eligible[j]:
+            chosen = j
+            j += 1
+    else:
+        candidates = np.flatnonzero(eligible)
+        chosen = int(candidates[-1]) if candidates.size else i_star
+    return chosen, i_star, eligible
+
+
+def solve_optimal_min_cluster_size(
+    paired: PairedGridScores,
+    *,
+    smooth_window: int = 3,
+    one_se_k: float = 1.0,
+    one_se_contiguous: bool = True,
+    one_se_min_samples: int = 6,
+    cluster_count_reward: float = 0.0,
+    n_entities: int | None = None,
+) -> SmoothedGridOptimumResult:
+    """
+    Choose ``min_cluster_size`` from per-sample grid scores with a paired one-SE rule.
+
+    1. Smooth each **sample's** curve with a centered moving average (linear, so the pooled
+       curve is unchanged, but the paired differences stay consistent with it).
+    2. Optionally add ``cluster_count_reward * log(n_clusters / n_ref)`` — deterministic per
+       grid point, so it shifts whole columns and leaves the paired standard errors alone.
+    3. Take ``i* = argmax`` of the pooled mean, then move to the **largest**
+       ``min_cluster_size`` reachable through consecutive grid points whose mean is within
+       ``one_se_k`` paired standard errors of ``i*``. Larger is the parsimonious direction:
+       fewer, denser, more reproducible clusters.
+
+    Step 3 is skipped below ``one_se_min_samples`` bootstrap samples, falling back to the
+    plain argmax. A standard error estimated from three numbers is itself mostly noise, and
+    acting on it makes the choice *less* reproducible, not more — measured on this repo's
+    grid exports, the slide starts paying for itself only around 6–8 samples. With a single
+    sample the paired standard errors are identically zero, so the fallback is automatic.
+    """
+    _validate_solver_args(
+        cluster_count_reward=cluster_count_reward,
+        n_entities=n_entities,
+        one_se_k=one_se_k,
+    )
+
+    x_all = np.array(paired.min_cluster_size, dtype=np.float64)
+    n_clusters_all = np.array(paired.n_clusters_mean, dtype=np.float64)
+    scores = np.asarray(paired.scores, dtype=np.float64)
+
+    cluster_term_all = _cluster_count_reward_term(
+        n_clusters_all,
+        cluster_count_reward=cluster_count_reward,
+        n_entities=n_entities,
+    )
+
+    smoothed = _uniform_centered_moving_average(scores, smooth_window)
+    y_all = smoothed + cluster_term_all[np.newaxis, :]
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        mu_all = np.nanmean(y_all, axis=0)
+
+    keep = np.isfinite(x_all) & np.isfinite(mu_all)
+    if not np.any(keep):
+        raise ValueError("No finite objective values on the min_cluster_size grid")
+
+    order = np.argsort(x_all[keep])
+    idx = np.flatnonzero(keep)[order]
+    x = x_all[idx]
+    mu = mu_all[idx]
+    y = y_all[:, idx]
+    raw = scores[:, idx]
+    n_clusters = n_clusters_all[idx]
+    cluster_term = cluster_term_all[idx]
+
+    se = _paired_standard_errors(y, int(np.nanargmax(mu)))
+    effective_k = one_se_k
+    if paired.n_samples < one_se_min_samples:
+        if one_se_k > 0 and paired.n_samples > 1:
+            logger.info(
+                "Only %d bootstrap sample(s) on the grid (< %d): standard errors are not "
+                "estimable, using the plain argmax instead of the one-SE rule.",
+                paired.n_samples,
+                one_se_min_samples,
+            )
+        effective_k = 0.0
+    chosen_idx, argmax_idx, eligible = _one_se_pick(
+        mu, se, one_se_k=effective_k, contiguous=one_se_contiguous
+    )
+
+    selection: SelectionKind
+    if paired.degenerate:
+        selection = "degenerate_fallback"
+    elif chosen_idx == argmax_idx:
+        selection = "argmax"
+    else:
+        selection = "one_se_paired"
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        raw_at = raw[:, chosen_idx]
+        raw_valid = raw_at[np.isfinite(raw_at)]
+        score_mean_at = float(np.mean(raw_valid)) if raw_valid.size else float("nan")
+        score_std_at = float(np.std(raw_valid, ddof=1)) if raw_valid.size > 1 else 0.0
+        y_objective = np.nanmean(raw, axis=0) + cluster_term
+
+    return SmoothedGridOptimumResult(
+        chosen_min_cluster_size=int(x[chosen_idx]),
+        score_mean_at_chosen=score_mean_at,
+        score_std_at_chosen=score_std_at,
+        n_clusters_mean_at_chosen=float(n_clusters[chosen_idx]),
+        x=tuple(float(v) for v in x),
+        y_objective=tuple(float(v) for v in y_objective),
+        y_cluster_term=tuple(float(v) for v in cluster_term),
+        y_smooth=tuple(float(v) for v in mu),
+        selection=selection,
+        y_se=tuple(float(v) for v in se),
+        y_eligible=tuple(bool(v) for v in eligible),
+        argmax_min_cluster_size=int(x[argmax_idx]),
+        one_se_k=float(effective_k),
+        n_clipped=paired.n_clipped,
+        n_samples=paired.n_samples,
+    )
+
+
+def solve_optimal_min_cluster_size_from_metrics_dfs(
+    metrics_dfs: Sequence[pd.DataFrame],
+    *,
+    objective: GridObjectiveSpec = "dbcv_ari_geomean",
+    smooth_window: int = 3,
+    one_se_k: float = 1.0,
+    one_se_contiguous: bool = True,
+    one_se_min_samples: int = 6,
+    cluster_count_reward: float = 0.0,
+    n_entities: int | None = None,
+) -> SmoothedGridOptimumResult:
+    """Build per-sample scores then solve. This is the preferred entry point."""
+    paired = build_paired_grid_scores(metrics_dfs, objective=objective)
+    return solve_optimal_min_cluster_size(
+        paired,
+        smooth_window=smooth_window,
+        one_se_k=one_se_k,
+        one_se_contiguous=one_se_contiguous,
+        one_se_min_samples=one_se_min_samples,
+        cluster_count_reward=cluster_count_reward,
+        n_entities=n_entities,
+    )
+
+
+def solve_optimal_min_cluster_size_from_aggregated(
+    report: AggregatedGridReport,
+    *,
+    objective: GridObjectiveSpec = "dbcv_ari_geomean",
+    smooth_window: int = 3,
+    one_se_k: float = 1.0,
+    one_se_contiguous: bool = True,
+    one_se_min_samples: int = 6,
+    cluster_count_reward: float = 0.0,
+    n_entities: int | None = None,
+) -> SmoothedGridOptimumResult:
+    """
+    Solve from already-pooled per-metric aggregates, for callers that no longer hold the
+    per-sample tables.
+
+    The pairing across grid points is unrecoverable here, so the standard errors fall back
+    to the unpaired ``sqrt(se_i**2 + se_istar**2)`` — wider than the paired version and
+    therefore more willing to slide toward larger ``min_cluster_size``. Prefer
+    :func:`solve_optimal_min_cluster_size_from_metrics_dfs` whenever the per-sample frames
+    are available.
+    """
+    if len(report.points) == 0:
+        raise ValueError("No aggregated grid points provided")
+    _validate_solver_args(
+        cluster_count_reward=cluster_count_reward,
+        n_entities=n_entities,
+        one_se_k=one_se_k,
+    )
+
+    md, sd, cd = _metric_vectors(report, "dbcv")
+    ma, sa, ca = _metric_vectors(report, "ari")
+    if objective == "dbcv_ari_geomean" and not np.any(np.isfinite(ma)):
+        logger.info(
+            "No ARI on the aggregated grid; falling back to a DBCV-only objective."
+        )
+        objective = "dbcv"
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        means = _combine_for_objective(md, ma, objective)
+    # Relative-error propagation through the geometric mean (delta method):
+    #   se_g / g = 0.5 * sqrt((se_d/d)**2 + (se_a/a)**2)
+    if objective == "dbcv_ari_geomean":
+        stds = geomean_std(md, sd, ma, sa)
+        counts = np.minimum(cd, ca)
+    elif objective == "ari":
+        stds, counts = sa, ca
+    else:
+        stds, counts = sd, cd
+
+    x_all = np.array([p.min_cluster_size for p in report.points], dtype=np.float64)
+    n_clusters_all = np.array(
+        [p.n_clusters_mean for p in report.points], dtype=np.float64
+    )
+    cluster_term_all = _cluster_count_reward_term(
+        n_clusters_all, cluster_count_reward=cluster_count_reward, n_entities=n_entities
+    )
+    smoothed = _uniform_centered_moving_average(means, smooth_window)
+    mu_all = smoothed + cluster_term_all
+
+    keep = np.isfinite(x_all) & np.isfinite(mu_all)
+    if not np.any(keep):
+        raise ValueError("No finite objective values in aggregated grid report")
+    order = np.argsort(x_all[keep])
+    idx = np.flatnonzero(keep)[order]
+    x, mu = x_all[idx], mu_all[idx]
+    base_means, base_stds = means[idx], stds[idx]
+    n_obs = np.maximum(counts[idx], 1.0)
+    n_clusters, cluster_term = n_clusters_all[idx], cluster_term_all[idx]
+
+    point_se = np.nan_to_num(base_stds, nan=0.0) / np.sqrt(n_obs)
+    i_star = int(np.nanargmax(mu))
+    se = np.sqrt(point_se**2 + point_se[i_star] ** 2)
+    se[i_star] = 0.0
+
+    n_samples = int(np.nanmax(counts)) if counts.size else 0
+    effective_k = 0.0 if n_samples < one_se_min_samples else one_se_k
+    chosen_idx, argmax_idx, eligible = _one_se_pick(
+        mu, se, one_se_k=effective_k, contiguous=one_se_contiguous
+    )
+
+    return SmoothedGridOptimumResult(
+        chosen_min_cluster_size=int(x[chosen_idx]),
+        score_mean_at_chosen=float(base_means[chosen_idx]),
+        score_std_at_chosen=float(base_stds[chosen_idx]),
+        n_clusters_mean_at_chosen=float(n_clusters[chosen_idx]),
+        x=tuple(float(v) for v in x),
+        y_objective=tuple(float(v) for v in (base_means + cluster_term)),
+        y_cluster_term=tuple(float(v) for v in cluster_term),
+        y_smooth=tuple(float(v) for v in mu),
+        selection="argmax" if chosen_idx == argmax_idx else "one_se_paired",
+        y_se=tuple(float(v) for v in se),
+        y_eligible=tuple(bool(v) for v in eligible),
+        argmax_min_cluster_size=int(x[argmax_idx]),
+        one_se_k=float(effective_k),
+        n_samples=n_samples,
+    )
+
+
+def aggregated_grid_report_to_dataframe(report: AggregatedGridReport) -> pd.DataFrame:
+    """Lossless round-trip style export for notebooks (typed report → table)."""
+    rows: list[dict[str, float | int]] = []
+    for p in report.points:
+        rows.append(
+            {
+                "min_cluster_size": p.min_cluster_size,
+                "dbcv_mean": p.dbcv.mean,
+                "dbcv_std": p.dbcv.std,
+                "dbcv_count": p.dbcv.count,
+                "icm_mean": p.icm_mean,
+                "n_clusters_mean": p.n_clusters_mean,
+                "ari_mean": p.ari.mean,
+                "ari_std": p.ari.std,
+                "ari_count": p.ari.count,
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def cosine_similarity_std(
+    tensor: torch.Tensor, max_pairs: int = 200_000, random_seed: int = 13
+) -> torch.Tensor:
+    """
+    Calculate the standard deviation of pairwise cosine similarities
+    for a tensor of shape (n_b, dim_emb).
+    """
+    normalized = F.normalize(tensor.float(), p=2, dim=1)
+
+    n_points = normalized.size(0)
+    if n_points < 2:
+        return torch.tensor(float("nan"), dtype=normalized.dtype)
+
+    total_pairs = n_points * (n_points - 1) // 2
+
+    if total_pairs <= max_pairs:
+        cos_sim_matrix = torch.mm(normalized, normalized.t())
+        triu_indices = torch.triu_indices(
+            cos_sim_matrix.size(0), cos_sim_matrix.size(1), offset=1
+        )
+        cos_similarities = cos_sim_matrix[triu_indices[0], triu_indices[1]]
+        return torch.std(cos_similarities)
+
+    sample_size = min(max_pairs, total_pairs)
+    generator = torch.Generator(device=normalized.device)
+    generator.manual_seed(random_seed)
+
+    idx_i = torch.randint(
+        0, n_points, (sample_size,), generator=generator, device=normalized.device
+    )
+    idx_j = torch.randint(
+        0, n_points - 1, (sample_size,), generator=generator, device=normalized.device
+    )
+    idx_j = idx_j + (idx_j >= idx_i).long()  # ensure i != j
+    cos_similarities = (normalized[idx_i] * normalized[idx_j]).sum(dim=1)
+    return torch.std(cos_similarities)
+
+
+def _adjusted_rand_index_vs_entity_codes(
+    entity_codes: np.ndarray,
+    cluster_labels: np.ndarray,
+) -> float:
+    """ARI between KB entity codes and HDBSCAN labels; noise (-1) excluded (matches analysis)."""
+    valid_mask = cluster_labels != -1
+    if not valid_mask.any():
+        return 0.0
+    y_true = entity_codes[valid_mask]
+    y_pred = cluster_labels[valid_mask]
+    if len(y_pred) == 0:
+        return 0.0
+    return float(adjusted_rand_score(y_true, y_pred))
+
+
+def evaluate_cluster_size_grid(
+    dfr2: pd.DataFrame,
+    umap_columns: list[str],
+    sizes: list[int],
+    max_pairs_per_cluster: int = 200_000,
+) -> pd.DataFrame:
+    """
+    Evaluate clustering metrics on a grid of min_cluster_size values.
+
+    Uses DBCV (Density-Based Clustering Validation) and, when ``entity`` is present,
+    adjusted Rand index vs. the reference labels (noise label -1 excluded). The reference
+    is ``entity_class`` when a class view was applied (:mod:`pelinker.kb.classes`),
+    otherwise the raw ``entity`` label.
+
+    Returns:
+        DataFrame with columns: min_cluster_size, icm, n_clusters, dbcv, ari
+    """
+    umap_values = dfr2[umap_columns].to_numpy(dtype=np.float32, copy=False)
+    entity_codes: np.ndarray | None = None
+    if "entity" in dfr2.columns:
+        entity_codes = (
+            dfr2[reference_label_column(dfr2)]
+            .astype("category")
+            .cat.codes.to_numpy(dtype=np.int64, copy=False)
+        )
+
+    metrics = []
+    for size in sizes:
+        clusterer = hdbscan.HDBSCAN(min_cluster_size=size, gen_min_span_tree=True)
+        labels = clusterer.fit_predict(umap_values)
+
+        ic = []
+        for ix in np.unique(labels):
+            if ix == -1:
+                continue
+            cluster_values = umap_values[labels == ix]
+            if len(cluster_values) < 2:
+                continue
+            tgroup = torch.from_numpy(cluster_values)
+            st = cosine_similarity_std(
+                tgroup, max_pairs=max_pairs_per_cluster, random_seed=13
+            )
+            ic += [float(st)]
+
+        icm = np.mean(ic) if ic else np.nan
+
+        unique_labels = set(labels)
+        n_clusters = len(unique_labels) - (1 if -1 in unique_labels else 0)
+
+        if n_clusters >= 2:
+            rv = getattr(clusterer, "relative_validity_", None)
+            dbcv = float(rv) if rv is not None else float("nan")
+        else:
+            dbcv = np.nan
+
+        if entity_codes is not None:
+            ari = _adjusted_rand_index_vs_entity_codes(entity_codes, labels)
+        else:
+            ari = float("nan")
+
+        if n_clusters >= 1:
+            metrics += [(size, icm, n_clusters, dbcv, ari)]
+
+    return pd.DataFrame(
+        metrics, columns=["min_cluster_size", "icm", "n_clusters", "dbcv", "ari"]
+    )
+
+
+def aggregate_grid_metrics(all_metrics_dfs: list[pd.DataFrame]) -> AggregatedGridReport:
+    """
+    Aggregate grid evaluation metrics across multiple samples into a typed report.
+
+    Per ``min_cluster_size`` we keep DBCV mean, std, and count (so uncertainty is not
+    discarded). ICM and cluster count are aggregated as means for diagnostics.
+    """
+    if not all_metrics_dfs:
+        return AggregatedGridReport(points=())
+
+    combined = pd.concat(all_metrics_dfs, ignore_index=True)
+    if "ari" not in combined.columns:
+        combined["ari"] = np.nan
+
+    aggregated = (
+        combined.groupby("min_cluster_size")
+        .agg(
+            {
+                "dbcv": ["mean", "std", "count"],
+                "icm": "mean",
+                "n_clusters": "mean",
+                "ari": ["mean", "std", "count"],
+            }
+        )
+        .reset_index()
+    )
+
+    aggregated.columns = [
+        "min_cluster_size",
+        "dbcv_mean",
+        "dbcv_std",
+        "dbcv_count",
+        "icm_mean",
+        "n_clusters_mean",
+        "ari_mean",
+        "ari_std",
+        "ari_count",
+    ]
+
+    aggregated["dbcv_std"] = aggregated["dbcv_std"].fillna(0.0)
+    aggregated["dbcv_count"] = aggregated["dbcv_count"].astype(int)
+    aggregated["ari_std"] = aggregated["ari_std"].fillna(0.0)
+    aggregated["ari_count"] = aggregated["ari_count"].astype(int)
+
+    points: list[AggregatedGridPoint] = []
+    for _, row in aggregated.sort_values("min_cluster_size").iterrows():
+        mcs = int(row["min_cluster_size"])
+        points.append(
+            AggregatedGridPoint(
+                min_cluster_size=mcs,
+                dbcv=ScalarMetricAggregate(
+                    mean=float(row["dbcv_mean"]),
+                    std=float(row["dbcv_std"]),
+                    count=int(row["dbcv_count"]),
+                ),
+                icm_mean=float(row["icm_mean"]),
+                n_clusters_mean=float(row["n_clusters_mean"]),
+                ari=ScalarMetricAggregate(
+                    mean=float(row["ari_mean"]),
+                    std=float(row["ari_std"]),
+                    count=int(row["ari_count"]),
+                ),
+            )
+        )
+
+    return AggregatedGridReport(points=tuple(points))
diff --git a/pelinker/clustering/metrics.py b/pelinker/clustering/metrics.py
new file mode 100644
index 0000000..d2cfc5f
--- /dev/null
+++ b/pelinker/clustering/metrics.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import pandas as pd
+from pelinker.kb.classes import reference_label_column
+from pelinker.reports.schema import ClusteringFitMetrics
+from sklearn.metrics import (
+    adjusted_rand_score,
+)
+
+
+"""Clustering quality metrics computed against known entity labels."""
+
+
+def compute_adjusted_rand_index(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """
+    Compute clustering quality via adjusted Rand index (ARI).
+
+    Args:
+        y_true: True labels (e.g., property names)
+        y_pred: Predicted cluster labels
+
+    Returns:
+        ARI score.
+    """
+    # Filter out noise points (label -1) for accuracy computation
+    valid_mask = y_pred != -1
+    if not valid_mask.any():
+        return 0.0
+
+    y_true_valid = y_true[valid_mask]
+    y_pred_valid = y_pred[valid_mask]
+
+    if len(y_true_valid) == 0:
+        return 0.0
+
+    ari = adjusted_rand_score(y_true_valid, y_pred_valid)
+    return float(ari)
+
+
+def compute_clustering_fit_metrics(
+    clusterer: object,
+    manifold_df: pd.DataFrame,
+    *,
+    min_cluster_size: int,
+    cluster_labels: np.ndarray,
+) -> ClusteringFitMetrics:
+    """DBCV, ARI vs ``entity``, and cluster counts for a fitted HDBSCAN model."""
+    labels = np.asarray(cluster_labels, dtype=np.int64).ravel()
+    n = int(labels.shape[0])
+    label_set = set(labels.tolist())
+    n_clusters_emergent = len(label_set) - (1 if -1 in label_set else 0)
+    noise_count = int(np.sum(labels == -1))
+    noise_fraction = float(noise_count) / float(n) if n > 0 else 0.0
+
+    rv = getattr(clusterer, "relative_validity_", None)
+    dbcv: float | None
+    if rv is None:
+        dbcv = None
+    else:
+        rv_f = float(rv)
+        if math.isnan(rv_f) or math.isinf(rv_f):
+            dbcv = None
+        else:
+            dbcv = rv_f
+
+    ari_score: float | None
+    if "entity" in manifold_df.columns and len(manifold_df) == n:
+        # The configured class view's key when one was applied (pelinker.kb.classes).
+        reference = reference_label_column(manifold_df)
+        property_labels = manifold_df[reference].astype("category").cat.codes.values
+        ari_score = compute_adjusted_rand_index(property_labels, labels)
+    else:
+        ari_score = None
+
+    return ClusteringFitMetrics(
+        min_cluster_size=min_cluster_size,
+        dbcv=dbcv,
+        ari=ari_score,
+        n_clusters_emergent=n_clusters_emergent,
+        noise_fraction=noise_fraction,
+        n_samples=n,
+    )
diff --git a/pelinker/clustering_search_ranking.py b/pelinker/clustering/ranking.py
similarity index 62%
rename from pelinker/clustering_search_ranking.py
rename to pelinker/clustering/ranking.py
index 159111e..086461e 100644
--- a/pelinker/clustering_search_ranking.py
+++ b/pelinker/clustering/ranking.py
@@ -4,11 +4,18 @@
 
 **Two-level criterion** (same in model selection and dim selection):
 
-1. **Inner (choose MCS)** — ``grid_objective=dbcv_ari_mean_minmax`` on the MCS
-   curve (min–max normalize mean DBCV and mean ARI, average, smooth, left plateau).
-2. **Outer (rank candidates)** — at each candidate's pooled MCS, take mean DBCV
-   and mean ARI, then combine with the same DBCV+ARI pooling used on the grid
-   (``dbcv_ari_mean_minmax`` across the leaderboard, or a resume-safe raw average).
+1. **Inner (choose MCS)** — ``grid_objective=dbcv_ari_geomean`` on the MCS curve
+   (clip DBCV and ARI at 0, take ``sqrt(dbcv * ari)``, smooth, then the paired
+   one-standard-error rule).
+2. **Outer (rank candidates)** — at each candidate's pooled MCS, combine mean DBCV
+   and mean ARI with **the same formula**.
+
+Using one scale-free formula at both levels is deliberate. The geometric mean's
+ranking is invariant to rescaling either metric, so nothing has to be normalized
+against the curve or against the leaderboard. That in turn makes ``outer_score``
+independent of which *other* candidates happened to be in the run, and comparable
+across runs — neither of which held while the outer level min–max normalized across
+the leaderboard.
 
 ``best_score`` in flat summary rows remains **mean DBCV** (for DBCV heatmaps).
 ``outer_score`` is the DBCV+ARI combo used to pick winners.
@@ -21,8 +28,8 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.clustering_grid import combine_two_metric_series
-from pelinker.reporting import ClusteringSearchSummaryRow
+from pelinker.clustering.grid import combine_clipped_geomean, geomean_std
+from pelinker.reports.schema import ClusteringSearchSummaryRow
 
 OUTER_SCORE_COL = "outer_score"
 OUTER_SCORE_STD_COL = "outer_score_std"
@@ -35,13 +42,14 @@
 METRICS_TWO_LEVEL_DOC = {
     "mcs": MCS_DOC,
     "inner_min_cluster_size": (
-        "grid_objective=dbcv_ari_mean_minmax: min-max normalize mean DBCV and mean ARI "
-        "on the MCS curve, average them, smooth, and pick the left plateau."
+        "grid_objective=dbcv_ari_geomean: clip mean DBCV and mean ARI at 0, take "
+        "sqrt(dbcv*ari) per bootstrap sample, smooth, then pick the largest MCS within "
+        "one paired standard error of the best."
     ),
     "outer_candidate_ranking": (
         "At the pooled consensus MCS, combine mean DBCV and mean ARI with the same "
-        "DBCV+ARI pooling (dbcv_ari_mean_minmax across candidates, or raw 0.5·(DBCV+ARI) "
-        "per row for resume-safe proxies). best_score stays mean DBCV for heatmaps; "
+        "clipped geometric mean used on the grid. Scale-free, so it does not depend on "
+        "which other candidates were in the run. best_score stays mean DBCV for heatmaps; "
         "outer_score ranks winners."
     ),
 }
@@ -55,18 +63,26 @@ def raw_outer_dbcv_ari_score(
     ari_std: float = 0.0,
 ) -> tuple[float, float]:
     """
-    Resume-safe per-candidate combine: ``0.5 * (dbcv + ari)`` when both finite.
+    Per-candidate combine: ``sqrt(max(dbcv, 0) * max(ari, 0))`` when both are finite.
 
-    Falls back to whichever metric is finite. Used for fusion proxies and mid-run
-    progress when cross-leaderboard min-max is not appropriate.
+    Falls back to whichever metric is finite. Depends only on this candidate's own
+    numbers, so it is safe for mid-run leaderboards and resumed runs.
     """
     dbcv_f = float(dbcv)
     dbcv_ok = np.isfinite(dbcv_f)
     ari_ok = ari is not None and np.isfinite(float(ari))
     if dbcv_ok and ari_ok:
-        ari_f = float(ari)
-        score = 0.5 * (dbcv_f + ari_f)
-        std = float(np.sqrt((float(dbcv_std) ** 2 + float(ari_std) ** 2) / 4.0))
+        score = float(
+            combine_clipped_geomean(np.array([dbcv_f]), np.array([float(ari)]))[0]
+        )
+        std = float(
+            geomean_std(
+                np.array([dbcv_f]),
+                np.array([float(dbcv_std)]),
+                np.array([float(ari)]),
+                np.array([float(ari_std)]),
+            )[0]
+        )
         return score, std
     if dbcv_ok:
         return dbcv_f, float(dbcv_std)
@@ -91,7 +107,6 @@ def outer_score_from_summary_row(row: ClusteringSearchSummaryRow) -> float:
 def attach_outer_scores(
     df: pd.DataFrame,
     *,
-    use_minmax: bool = True,
     dbcv_col: str = "best_score",
     dbcv_std_col: str = "best_score_std",
     ari_col: str = "ari",
@@ -100,9 +115,9 @@ def attach_outer_scores(
     """
     Add ``outer_score`` / ``outer_score_std`` columns.
 
-    When ``use_minmax`` is true (default), min–max normalize DBCV and ARI across
-    rows then average — same pooling as ``dbcv_ari_mean_minmax`` on an MCS grid.
-    When false, use the resume-safe raw average per row.
+    Each row is scored from its own DBCV and ARI alone with the clipped geometric mean —
+    the same formula the MCS grid uses. Nothing is normalized across rows, so adding or
+    removing an unrelated candidate cannot reorder the others.
     """
     out = df.copy()
     n = len(out)
@@ -134,15 +149,15 @@ def attach_outer_scores(
         ma = np.full(n, np.nan, dtype=np.float64)
         sa = np.zeros(n, dtype=np.float64)
 
-    cd = np.ones(n, dtype=np.float64)
-    ca = np.ones(n, dtype=np.float64)
-    # Treat missing ARI counts as 0 so combine falls back to DBCV-only.
-    ca = np.where(np.isfinite(ma), 1.0, 0.0)
-    cd = np.where(np.isfinite(md), 1.0, 0.0)
+    both = np.isfinite(md) & np.isfinite(ma)
+    means = np.where(both, combine_clipped_geomean(md, ma), np.nan)
+    stds = np.where(both, geomean_std(md, sd, ma, sa), np.nan)
+    # Fall back to whichever metric a row actually has, matching raw_outer_dbcv_ari_score.
+    only_dbcv = np.isfinite(md) & ~np.isfinite(ma)
+    only_ari = np.isfinite(ma) & ~np.isfinite(md)
+    means = np.where(only_dbcv, md, np.where(only_ari, ma, means))
+    stds = np.where(only_dbcv, sd, np.where(only_ari, sa, stds))
 
-    means, stds, _counts = combine_two_metric_series(
-        md, sd, cd, ma, sa, ca, use_minmax=use_minmax
-    )
     out[OUTER_SCORE_COL] = means
     out[OUTER_SCORE_STD_COL] = stds
     return out
@@ -154,14 +169,13 @@ def pick_best_row(
     score_col: str = OUTER_SCORE_COL,
     std_col: str = OUTER_SCORE_STD_COL,
     tie_break_cols: Sequence[str] = (),
-    use_minmax: bool = True,
 ) -> dict[str, object]:
     """
     Rank by outer DBCV+ARI score (descending), then lower std, then tie-break columns ascending.
     """
     if df.empty:
         raise ValueError("df must be a non-empty DataFrame")
-    ranked_base = attach_outer_scores(df, use_minmax=use_minmax)
+    ranked_base = attach_outer_scores(df)
     if score_col not in ranked_base.columns:
         raise ValueError(f"missing score column {score_col!r}")
 
diff --git a/pelinker/clustering/stability.py b/pelinker/clustering/stability.py
new file mode 100644
index 0000000..dd89005
--- /dev/null
+++ b/pelinker/clustering/stability.py
@@ -0,0 +1,241 @@
+"""Cluster-identity stability across bootstrap resamples.
+
+The submitted evaluation reported a cluster *count* with a spread (e.g. 63 ± 4.4) and
+nothing about whether the clusters themselves are the same objects from draw to draw. A
+count can be perfectly stable while every cluster's membership churns, so the count alone
+does not support "these are the emergent predicate senses".
+
+This module answers the identity question. Given per-bootstrap cluster assignments over a
+shared row space, it matches clusters *between* pairs of draws with the Hungarian
+algorithm — maximizing total member overlap (Jaccard) — and reports:
+
+- **per-cluster persistence** — for each cluster of a reference draw, the mean Jaccard of
+  its best match across the other draws, and how often it is matched at all;
+- **matched-cluster counts** — how many clusters survive a Jaccard floor per pair;
+- **cross-draw ARI** — agreement of two draws' partitions on the rows they share, which is
+  matching-free and so a useful independent check on the matched numbers.
+
+Only rows *shared* by two draws are compared: bootstrap draws overlap partially, and
+scoring a cluster on rows one draw never saw would report resampling as instability.
+
+Reading the numbers: Jaccard has a non-trivial chance floor when the number of clusters
+is small — two equal clusters over the same rows overlap ~0.5 under random reassignment —
+so a persistence figure is only interpretable against the cluster count it was measured
+at, and `jaccard_floor` should sit well above chance for that count. `ari_shared` is
+matching-free and does not share this failure mode, which is why both are reported.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Mapping, Sequence
+
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from sklearn.metrics import adjusted_rand_score
+
+from pelinker.clustering.composition import HDBSCAN_NOISE_CLUSTER_ID
+
+RowLabels = Mapping[Any, int]
+"""``row_key -> cluster_id`` for one bootstrap draw (noise included; filtered here)."""
+
+
+def _emergent(labels: RowLabels) -> dict[Any, int]:
+    return {
+        key: int(cluster)
+        for key, cluster in labels.items()
+        if int(cluster) != HDBSCAN_NOISE_CLUSTER_ID
+    }
+
+
+def _members_by_cluster(labels: Mapping[Any, int]) -> dict[int, set]:
+    out: dict[int, set] = {}
+    for key, cluster in labels.items():
+        out.setdefault(int(cluster), set()).add(key)
+    return out
+
+
+def jaccard(a: set, b: set) -> float:
+    if not a and not b:
+        return 0.0
+    return len(a & b) / len(a | b)
+
+
+@dataclass(frozen=True)
+class PairMatching:
+    """Hungarian matching between two draws, restricted to their shared rows."""
+
+    n_shared_rows: int
+    n_clusters_a: int
+    n_clusters_b: int
+    matches: tuple[tuple[int, int, float], ...]
+    """``(cluster_a, cluster_b, jaccard)`` for each matched pair."""
+    ari_shared: float | None
+
+    def matched_above(self, floor: float) -> int:
+        return sum(1 for _, _, j in self.matches if j >= floor)
+
+    def to_jsonable(self) -> dict[str, Any]:
+        return {
+            "n_shared_rows": self.n_shared_rows,
+            "n_clusters_a": self.n_clusters_a,
+            "n_clusters_b": self.n_clusters_b,
+            "n_matched": len(self.matches),
+            "mean_jaccard": (
+                float(np.mean([j for _, _, j in self.matches]))
+                if self.matches
+                else None
+            ),
+            "ari_shared": self.ari_shared,
+        }
+
+
+def match_two_draws(labels_a: RowLabels, labels_b: RowLabels) -> PairMatching:
+    """Optimal cluster correspondence between two draws by shared-member Jaccard."""
+    a = _emergent(labels_a)
+    b = _emergent(labels_b)
+    shared = set(a) & set(b)
+
+    clusters_a = _members_by_cluster({k: a[k] for k in shared})
+    clusters_b = _members_by_cluster({k: b[k] for k in shared})
+    ids_a = sorted(clusters_a)
+    ids_b = sorted(clusters_b)
+
+    if not ids_a or not ids_b:
+        return PairMatching(
+            n_shared_rows=len(shared),
+            n_clusters_a=len(ids_a),
+            n_clusters_b=len(ids_b),
+            matches=(),
+            ari_shared=None,
+        )
+
+    scores = np.zeros((len(ids_a), len(ids_b)), dtype=float)
+    for i, ca in enumerate(ids_a):
+        for j, cb in enumerate(ids_b):
+            scores[i, j] = jaccard(clusters_a[ca], clusters_b[cb])
+
+    # Hungarian on the negated overlap: maximize total Jaccard across the assignment.
+    rows, cols = linear_sum_assignment(-scores)
+    matches = tuple(
+        (ids_a[i], ids_b[j], float(scores[i, j]))
+        for i, j in zip(rows, cols)
+        if scores[i, j] > 0.0
+    )
+
+    ordered = sorted(shared)
+    ari = (
+        float(
+            adjusted_rand_score(
+                [a[k] for k in ordered],
+                [b[k] for k in ordered],
+            )
+        )
+        if ordered
+        else None
+    )
+    return PairMatching(
+        n_shared_rows=len(shared),
+        n_clusters_a=len(ids_a),
+        n_clusters_b=len(ids_b),
+        matches=matches,
+        ari_shared=ari,
+    )
+
+
+@dataclass(frozen=True)
+class StabilityReport:
+    """Aggregate identity stability over all ordered draw pairs."""
+
+    n_draws: int
+    jaccard_floor: float
+    cluster_counts: tuple[int, ...]
+    pairs: tuple[dict[str, Any], ...]
+    per_cluster: dict[int, dict[str, float]]
+    """Reference-draw cluster id → ``{mean_jaccard, match_rate}`` across other draws."""
+
+    def to_jsonable(self) -> dict[str, Any]:
+        mean_j = [
+            p["mean_jaccard"] for p in self.pairs if p["mean_jaccard"] is not None
+        ]
+        aris = [p["ari_shared"] for p in self.pairs if p["ari_shared"] is not None]
+        persistent = [
+            cid
+            for cid, stats in self.per_cluster.items()
+            if stats["mean_jaccard"] >= self.jaccard_floor
+        ]
+        return {
+            "n_draws": self.n_draws,
+            "jaccard_floor": self.jaccard_floor,
+            "cluster_count_mean": float(np.mean(self.cluster_counts))
+            if self.cluster_counts
+            else None,
+            "cluster_count_std": float(np.std(self.cluster_counts, ddof=1))
+            if len(self.cluster_counts) > 1
+            else None,
+            "n_pairs": len(self.pairs),
+            "mean_pair_jaccard": float(np.mean(mean_j)) if mean_j else None,
+            "mean_cross_draw_ari": float(np.mean(aris)) if aris else None,
+            "n_reference_clusters": len(self.per_cluster),
+            "n_persistent_clusters": len(persistent),
+            "persistent_fraction": (
+                len(persistent) / len(self.per_cluster) if self.per_cluster else None
+            ),
+        }
+
+
+def analyze_stability(
+    draws: Sequence[RowLabels],
+    *,
+    jaccard_floor: float = 0.5,
+    reference_index: int = 0,
+) -> StabilityReport:
+    """Match every draw pair and summarize how well cluster identity persists.
+
+    Args:
+        draws: One ``row_key -> cluster_id`` mapping per bootstrap draw.
+        jaccard_floor: Overlap at or above which a cluster counts as the "same" cluster.
+        reference_index: Draw whose clusters the per-cluster persistence is reported for.
+    """
+    if len(draws) < 2:
+        raise ValueError("stability needs at least two draws")
+
+    counts = tuple(len(_members_by_cluster(_emergent(d))) for d in draws)
+
+    pairs: list[dict[str, Any]] = []
+    for i in range(len(draws)):
+        for j in range(i + 1, len(draws)):
+            matching = match_two_draws(draws[i], draws[j])
+            payload = matching.to_jsonable()
+            payload["draw_a"] = i
+            payload["draw_b"] = j
+            payload["n_matched_above_floor"] = matching.matched_above(jaccard_floor)
+            pairs.append(payload)
+
+    reference = _emergent(draws[reference_index])
+    ref_clusters = sorted(_members_by_cluster(reference))
+    per_cluster: dict[int, dict[str, float]] = {}
+    for cid in ref_clusters:
+        jaccards: list[float] = []
+        for k, other in enumerate(draws):
+            if k == reference_index:
+                continue
+            matching = match_two_draws(draws[reference_index], other)
+            best = [j for a, _, j in matching.matches if a == cid]
+            jaccards.append(best[0] if best else 0.0)
+        per_cluster[cid] = {
+            "mean_jaccard": float(np.mean(jaccards)) if jaccards else 0.0,
+            "match_rate": (
+                float(np.mean([j >= jaccard_floor for j in jaccards]))
+                if jaccards
+                else 0.0
+            ),
+        }
+
+    return StabilityReport(
+        n_draws=len(draws),
+        jaccard_floor=jaccard_floor,
+        cluster_counts=counts,
+        pairs=tuple(pairs),
+        per_cluster=per_cluster,
+    )
diff --git a/pelinker/transform.py b/pelinker/clustering/transform.py
similarity index 96%
rename from pelinker/transform.py
rename to pelinker/clustering/transform.py
index 856d987..abcaac2 100644
--- a/pelinker/transform.py
+++ b/pelinker/clustering/transform.py
@@ -13,10 +13,18 @@
 
 import numpy as np
 import pandas as pd
-import umap
 from sklearn.decomposition import PCA
 
-from pelinker.config import TransformConfig
+from pelinker.core.config import TransformConfig
+from pelinker.core.runtime import preload_torch_before_tensorflow
+
+# ``import umap`` pulls in TensorFlow (tf-keras, for ParametricUMAP). Torch and its triton
+# runtime have to be in the process first: with TensorFlow initialised first, a later
+# ``import triton.runtime`` segfaults the interpreter. This is the single place that
+# ordering is enforced — see pelinker.core.runtime.
+preload_torch_before_tensorflow()
+
+import umap  # noqa: E402  (must follow the preload above)
 
 logger = logging.getLogger(__name__)
 
diff --git a/pelinker/clustering_grid.py b/pelinker/clustering_grid.py
deleted file mode 100644
index c87a68e..0000000
--- a/pelinker/clustering_grid.py
+++ /dev/null
@@ -1,577 +0,0 @@
-"""HDBSCAN min_cluster_size grid evaluation, cross-sample aggregation, and smooth optimum selection."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Literal
-
-from pelinker.config import GridObjectiveSpec
-
-import hdbscan
-import numpy as np
-import pandas as pd
-import torch
-from sklearn.metrics import adjusted_rand_score
-from torch.nn import functional as F
-
-
-@dataclass(frozen=True)
-class ScalarMetricAggregate:
-    """Mean, dispersion, and sample count for one metric at a single grid point."""
-
-    mean: float
-    std: float
-    count: int
-
-
-@dataclass(frozen=True)
-class AggregatedGridPoint:
-    """One grid value of ``min_cluster_size`` with aggregated metrics across samples."""
-
-    min_cluster_size: int
-    dbcv: ScalarMetricAggregate
-    icm_mean: float
-    n_clusters_mean: float
-    ari: ScalarMetricAggregate
-
-
-@dataclass(frozen=True)
-class AggregatedGridReport:
-    """Typed aggregation of per-sample grid metrics; points are sorted by ``min_cluster_size``."""
-
-    points: tuple[AggregatedGridPoint, ...]
-
-    def __post_init__(self) -> None:
-        sizes = [p.min_cluster_size for p in self.points]
-        if sizes != sorted(sizes):
-            raise ValueError(
-                "AggregatedGridReport.points must be sorted by min_cluster_size"
-            )
-        if len(set(sizes)) != len(sizes):
-            raise ValueError(
-                "Duplicate min_cluster_size in AggregatedGridReport.points"
-            )
-
-
-@dataclass(frozen=True)
-class SmoothedGridOptimumResult:
-    """Diagnostics for ``solve_optimal_min_cluster_size_from_aggregated``.
-
-    ``score_mean_at_chosen`` / ``score_std_at_chosen`` refer to the raw objective (before
-    smoothing) at the chosen grid point.
-    """
-
-    chosen_min_cluster_size: int
-    score_mean_at_chosen: float
-    score_std_at_chosen: float
-    n_clusters_mean_at_chosen: float
-    x: tuple[float, ...]
-    y_objective: tuple[float, ...]
-    y_cluster_term: tuple[float, ...]
-    y_smooth: tuple[float, ...]
-    dy_dx: tuple[float, ...]
-    d2y_dx2: tuple[float, ...]
-    selection: Literal["plateau_derivative", "smoothed_argmax"]
-
-
-def _ensure_odd_window(window: int) -> int:
-    if window < 1:
-        raise ValueError("smooth window must be >= 1")
-    return window if window % 2 == 1 else window + 1
-
-
-def _precision_weights(
-    std: np.ndarray, count: np.ndarray, eps: float = 1e-8
-) -> np.ndarray:
-    """Inverse-variance style weights; uses sample count as an extra multiplier."""
-    var = std * std + eps
-    return count.astype(float) / var
-
-
-def _weighted_centered_moving_average(
-    values: np.ndarray,
-    point_weights: np.ndarray,
-    window: int,
-) -> np.ndarray:
-    """Centered moving average; within each window, averages are weighted by ``point_weights``."""
-    w = _ensure_odd_window(window)
-    half = w // 2
-    n = len(values)
-    out = np.empty(n, dtype=np.float64)
-    for i in range(n):
-        lo = max(0, i - half)
-        hi = min(n, i + half + 1)
-        pw = point_weights[lo:hi]
-        vv = values[lo:hi]
-        denom = float(np.sum(pw))
-        out[i] = float(np.sum(pw * vv) / denom) if denom > 0 else float("nan")
-    return out
-
-
-def _uniform_centered_moving_average(values: np.ndarray, window: int) -> np.ndarray:
-    w = _ensure_odd_window(window)
-    half = w // 2
-    n = len(values)
-    out = np.empty(n, dtype=np.float64)
-    for i in range(n):
-        lo = max(0, i - half)
-        hi = min(n, i + half + 1)
-        out[i] = float(np.mean(values[lo:hi]))
-    return out
-
-
-def _metric_vectors(
-    report: AggregatedGridReport,
-    which: Literal["dbcv", "ari"],
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    if which == "dbcv":
-        means = np.array([p.dbcv.mean for p in report.points], dtype=np.float64)
-        stds = np.array([p.dbcv.std for p in report.points], dtype=np.float64)
-        counts = np.array([p.dbcv.count for p in report.points], dtype=np.float64)
-    else:
-        means = np.array([p.ari.mean for p in report.points], dtype=np.float64)
-        stds = np.array([p.ari.std for p in report.points], dtype=np.float64)
-        counts = np.array([p.ari.count for p in report.points], dtype=np.float64)
-    return means, stds, counts
-
-
-def _minmax_norm_per_curve(values: np.ndarray, *, eps: float = 1e-12) -> np.ndarray:
-    """Per-grid min–max to [0, 1] using finite values only; constant curves → 1.0."""
-    out = np.full_like(values, np.nan, dtype=np.float64)
-    finite = np.isfinite(values)
-    if not np.any(finite):
-        return out
-    lo = float(np.min(values[finite]))
-    hi = float(np.max(values[finite]))
-    if hi - lo <= eps:
-        out[finite] = 1.0
-    else:
-        out[finite] = (values[finite] - lo) / (hi - lo)
-    return out
-
-
-def combine_two_metric_series(
-    m1: np.ndarray,
-    s1: np.ndarray,
-    c1: np.ndarray,
-    m2: np.ndarray,
-    s2: np.ndarray,
-    c2: np.ndarray,
-    *,
-    use_minmax: bool,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """
-    Pairwise combine two metrics (same length). Missing values use the other series only.
-
-    When ``use_minmax`` is true, each series is min–max normalized across the curve
-    (or leaderboard) before averaging — same pooling as ``dbcv_ari_mean_minmax``.
-    """
-    n = len(m1)
-    means = np.empty(n, dtype=np.float64)
-    stds = np.empty(n, dtype=np.float64)
-    counts = np.empty(n, dtype=np.float64)
-    v1 = _minmax_norm_per_curve(m1) if use_minmax else m1
-    v2 = _minmax_norm_per_curve(m2) if use_minmax else m2
-    for i in range(n):
-        f1 = np.isfinite(v1[i]) and np.isfinite(m1[i])
-        f2 = np.isfinite(v2[i]) and np.isfinite(m2[i])
-        if f1 and f2:
-            means[i] = 0.5 * (v1[i] + v2[i])
-            stds[i] = float(np.sqrt((s1[i] * s1[i] + s2[i] * s2[i]) / 4.0))
-            counts[i] = float(min(c1[i], c2[i]))
-        elif f1:
-            means[i] = v1[i]
-            stds[i] = float(s1[i])
-            counts[i] = float(c1[i])
-        elif f2:
-            means[i] = v2[i]
-            stds[i] = float(s2[i])
-            counts[i] = float(c2[i])
-        else:
-            means[i] = float("nan")
-            stds[i] = float("nan")
-            counts[i] = 0.0
-    return means, stds, counts
-
-
-def _objective_mean_std_count(
-    report: AggregatedGridReport,
-    objective: GridObjectiveSpec,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    if objective == "dbcv":
-        return _metric_vectors(report, "dbcv")
-    if objective == "ari":
-        return _metric_vectors(report, "ari")
-    md, sd, cd = _metric_vectors(report, "dbcv")
-    ma, sa, ca = _metric_vectors(report, "ari")
-    if objective == "dbcv_ari_mean_minmax":
-        return combine_two_metric_series(md, sd, cd, ma, sa, ca, use_minmax=True)
-    if objective == "dbcv_ari_mean_raw":
-        return combine_two_metric_series(md, sd, cd, ma, sa, ca, use_minmax=False)
-    raise ValueError(f"Unknown grid objective: {objective!r}")
-
-
-def _apply_optimization_method(
-    means: np.ndarray,
-    stds: np.ndarray,
-    method: str,
-    uncertainty_penalty: float,
-) -> np.ndarray:
-    if method == "mean":
-        return means.copy()
-    if method == "lower_bound":
-        return means - uncertainty_penalty * stds
-    if method == "weighted":
-        return means.copy()
-    raise ValueError(f"Unknown optimization method: {method!r}")
-
-
-def _cluster_count_reward_term(
-    n_clusters: np.ndarray,
-    *,
-    cluster_count_reward: float,
-    n_entities: int | None,
-) -> np.ndarray:
-    """Additive log penalty: 0 at ``n_ref`` clusters, negative when fewer clusters."""
-    if cluster_count_reward == 0.0:
-        return np.zeros_like(n_clusters, dtype=np.float64)
-    n_clust = np.maximum(n_clusters.astype(np.float64), 1.0)
-    n_ref = float(n_entities) if n_entities is not None else float(np.max(n_clust))
-    if n_ref <= 0:
-        return np.zeros_like(n_clust, dtype=np.float64)
-    return cluster_count_reward * np.log(n_clust / n_ref)
-
-
-def solve_optimal_min_cluster_size_from_aggregated(
-    report: AggregatedGridReport,
-    *,
-    objective: GridObjectiveSpec = "dbcv",
-    method: str = "mean",
-    uncertainty_penalty: float = 1.0,
-    smooth_window: int = 3,
-    plateau_fraction: float = 0.92,
-    derivative_rel_tol: float = 0.12,
-    precision_weighted_smooth: bool | None = None,
-    cluster_count_reward: float = 0.0,
-    n_entities: int | None = None,
-) -> SmoothedGridOptimumResult:
-    """
-    Choose ``min_cluster_size`` from aggregated noisy grid scores.
-
-    Builds f(x) from ``objective`` (single metric or pooled DBCV+ARI), then optionally
-    ``method`` (mean / lower_bound / weighted). When ``cluster_count_reward`` > 0, adds
-    ``cluster_count_reward * log(n_clusters / n_ref)`` using mean cluster counts per grid
-    point (``n_ref`` = ``n_entities`` when set, else the maximum mean cluster count on the
-    grid). Smooths f with a centered moving average, then prefers the **leftmost** x where
-    the smoothed curve is near the top of its **range** (f ≥ ``y_min + plateau_fraction ·
-    (y_max - y_min)`` on the smoothed curve, with ``|df/dx|`` small). If none qualify, uses
-    the smoothed argmax.
-
-    ``precision_weighted_smooth`` defaults to True for ``lower_bound`` and ``weighted``,
-    and False for ``mean``.
-    """
-    if len(report.points) == 0:
-        raise ValueError("No aggregated grid points provided")
-    if cluster_count_reward < 0:
-        raise ValueError("cluster_count_reward must be >= 0")
-    if n_entities is not None and n_entities < 1:
-        raise ValueError("n_entities must be >= 1 when provided")
-
-    x = np.array([p.min_cluster_size for p in report.points], dtype=np.float64)
-    n_clusters_all = np.array(
-        [p.n_clusters_mean for p in report.points], dtype=np.float64
-    )
-    base_means, stds, counts = _objective_mean_std_count(report, objective)
-    y = _apply_optimization_method(base_means, stds, method, uncertainty_penalty)
-    cluster_term_all = _cluster_count_reward_term(
-        n_clusters_all,
-        cluster_count_reward=cluster_count_reward,
-        n_entities=n_entities,
-    )
-    y = y + cluster_term_all
-
-    finite = np.isfinite(x) & np.isfinite(y)
-    if not np.any(finite):
-        raise ValueError("No finite objective values in aggregated grid report")
-
-    x = x[finite]
-    y = y[finite]
-    base_means = base_means[finite]
-    stds = stds[finite]
-    counts = counts[finite]
-    n_clusters = n_clusters_all[finite]
-    cluster_term = cluster_term_all[finite]
-    order = np.argsort(x)
-    x = x[order]
-    y = y[order]
-    base_means = base_means[order]
-    stds = stds[order]
-    counts = counts[order]
-    n_clusters = n_clusters[order]
-    cluster_term = cluster_term[order]
-
-    if precision_weighted_smooth is None:
-        precision_weighted_smooth = method in ("lower_bound", "weighted")
-
-    if precision_weighted_smooth:
-        pweights = _precision_weights(stds, counts)
-        y_s = _weighted_centered_moving_average(y, pweights, smooth_window)
-    else:
-        y_s = _uniform_centered_moving_average(y, smooth_window)
-
-    dydx = np.gradient(y_s, x)
-    d2ydx2 = np.gradient(dydx, x)
-
-    smooth_finite = np.isfinite(y_s)
-    if not np.any(smooth_finite):
-        raise ValueError("Smoothed objective is non-finite")
-    y_max = float(np.max(y_s[smooth_finite]))
-    y_min = float(np.min(y_s[smooth_finite]))
-    if not np.isfinite(y_max):
-        raise ValueError("Smoothed objective is non-finite")
-
-    abs_dydx = np.abs(dydx)
-    scale = float(np.nanmax(abs_dydx))
-    if not np.isfinite(scale) or scale <= 0:
-        scale = 1.0
-    thresh = derivative_rel_tol * scale
-    level = y_min + plateau_fraction * (y_max - y_min)
-
-    chosen_idx: int | None = None
-    selection: Literal["plateau_derivative", "smoothed_argmax"] = "smoothed_argmax"
-    for i in range(len(x)):
-        if not np.isfinite(y_s[i]) or not np.isfinite(dydx[i]):
-            continue
-        if y_s[i] >= level and abs_dydx[i] <= thresh:
-            chosen_idx = i
-            selection = "plateau_derivative"
-            break
-
-    if chosen_idx is None:
-        chosen_idx = int(np.nanargmax(y_s))
-
-    chosen_x = int(x[chosen_idx])
-    score_mean_at = float(base_means[chosen_idx])
-    score_std_at = float(stds[chosen_idx])
-    n_clusters_at = float(n_clusters[chosen_idx])
-
-    return SmoothedGridOptimumResult(
-        chosen_min_cluster_size=chosen_x,
-        score_mean_at_chosen=score_mean_at,
-        score_std_at_chosen=score_std_at,
-        n_clusters_mean_at_chosen=n_clusters_at,
-        x=tuple(float(v) for v in x),
-        y_objective=tuple(float(v) for v in y),
-        y_cluster_term=tuple(float(v) for v in cluster_term),
-        y_smooth=tuple(float(v) for v in y_s),
-        dy_dx=tuple(float(v) for v in dydx),
-        d2y_dx2=tuple(float(v) for v in d2ydx2),
-        selection=selection,
-    )
-
-
-def aggregated_grid_report_to_dataframe(report: AggregatedGridReport) -> pd.DataFrame:
-    """Lossless round-trip style export for notebooks (typed report → table)."""
-    rows: list[dict[str, float | int]] = []
-    for p in report.points:
-        rows.append(
-            {
-                "min_cluster_size": p.min_cluster_size,
-                "dbcv_mean": p.dbcv.mean,
-                "dbcv_std": p.dbcv.std,
-                "dbcv_count": p.dbcv.count,
-                "icm_mean": p.icm_mean,
-                "n_clusters_mean": p.n_clusters_mean,
-                "ari_mean": p.ari.mean,
-                "ari_std": p.ari.std,
-                "ari_count": p.ari.count,
-            }
-        )
-    return pd.DataFrame(rows)
-
-
-def cosine_similarity_std(
-    tensor: torch.Tensor, max_pairs: int = 200_000, random_seed: int = 13
-) -> torch.Tensor:
-    """
-    Calculate the standard deviation of pairwise cosine similarities
-    for a tensor of shape (n_b, dim_emb).
-    """
-    normalized = F.normalize(tensor.float(), p=2, dim=1)
-
-    n_points = normalized.size(0)
-    if n_points < 2:
-        return torch.tensor(float("nan"), dtype=normalized.dtype)
-
-    total_pairs = n_points * (n_points - 1) // 2
-
-    if total_pairs <= max_pairs:
-        cos_sim_matrix = torch.mm(normalized, normalized.t())
-        triu_indices = torch.triu_indices(
-            cos_sim_matrix.size(0), cos_sim_matrix.size(1), offset=1
-        )
-        cos_similarities = cos_sim_matrix[triu_indices[0], triu_indices[1]]
-        return torch.std(cos_similarities)
-
-    sample_size = min(max_pairs, total_pairs)
-    generator = torch.Generator(device=normalized.device)
-    generator.manual_seed(random_seed)
-
-    idx_i = torch.randint(
-        0, n_points, (sample_size,), generator=generator, device=normalized.device
-    )
-    idx_j = torch.randint(
-        0, n_points - 1, (sample_size,), generator=generator, device=normalized.device
-    )
-    idx_j = idx_j + (idx_j >= idx_i).long()  # ensure i != j
-    cos_similarities = (normalized[idx_i] * normalized[idx_j]).sum(dim=1)
-    return torch.std(cos_similarities)
-
-
-def _adjusted_rand_index_vs_entity_codes(
-    entity_codes: np.ndarray,
-    cluster_labels: np.ndarray,
-) -> float:
-    """ARI between KB entity codes and HDBSCAN labels; noise (-1) excluded (matches analysis)."""
-    valid_mask = cluster_labels != -1
-    if not valid_mask.any():
-        return 0.0
-    y_true = entity_codes[valid_mask]
-    y_pred = cluster_labels[valid_mask]
-    if len(y_pred) == 0:
-        return 0.0
-    return float(adjusted_rand_score(y_true, y_pred))
-
-
-def evaluate_cluster_size_grid(
-    dfr2: pd.DataFrame,
-    umap_columns: list[str],
-    sizes: list[int],
-    max_pairs_per_cluster: int = 200_000,
-) -> pd.DataFrame:
-    """
-    Evaluate clustering metrics on a grid of min_cluster_size values.
-
-    Uses DBCV (Density-Based Clustering Validation) and, when ``entity`` is present,
-    adjusted Rand index vs. entity codes (noise label -1 excluded).
-
-    Returns:
-        DataFrame with columns: min_cluster_size, icm, n_clusters, dbcv, ari
-    """
-    umap_values = dfr2[umap_columns].to_numpy(dtype=np.float32, copy=False)
-    entity_codes: np.ndarray | None = None
-    if "entity" in dfr2.columns:
-        entity_codes = (
-            dfr2["entity"]
-            .astype("category")
-            .cat.codes.to_numpy(dtype=np.int64, copy=False)
-        )
-
-    metrics = []
-    for size in sizes:
-        clusterer = hdbscan.HDBSCAN(min_cluster_size=size, gen_min_span_tree=True)
-        labels = clusterer.fit_predict(umap_values)
-
-        ic = []
-        for ix in np.unique(labels):
-            if ix == -1:
-                continue
-            cluster_values = umap_values[labels == ix]
-            if len(cluster_values) < 2:
-                continue
-            tgroup = torch.from_numpy(cluster_values)
-            st = cosine_similarity_std(
-                tgroup, max_pairs=max_pairs_per_cluster, random_seed=13
-            )
-            ic += [float(st)]
-
-        icm = np.mean(ic) if ic else np.nan
-
-        unique_labels = set(labels)
-        n_clusters = len(unique_labels) - (1 if -1 in unique_labels else 0)
-
-        if n_clusters >= 2:
-            rv = getattr(clusterer, "relative_validity_", None)
-            dbcv = float(rv) if rv is not None else float("nan")
-        else:
-            dbcv = np.nan
-
-        if entity_codes is not None:
-            ari = _adjusted_rand_index_vs_entity_codes(entity_codes, labels)
-        else:
-            ari = float("nan")
-
-        if n_clusters >= 1:
-            metrics += [(size, icm, n_clusters, dbcv, ari)]
-
-    return pd.DataFrame(
-        metrics, columns=["min_cluster_size", "icm", "n_clusters", "dbcv", "ari"]
-    )
-
-
-def aggregate_grid_metrics(all_metrics_dfs: list[pd.DataFrame]) -> AggregatedGridReport:
-    """
-    Aggregate grid evaluation metrics across multiple samples into a typed report.
-
-    Per ``min_cluster_size`` we keep DBCV mean, std, and count (so uncertainty is not
-    discarded). ICM and cluster count are aggregated as means for diagnostics.
-    """
-    if not all_metrics_dfs:
-        return AggregatedGridReport(points=())
-
-    combined = pd.concat(all_metrics_dfs, ignore_index=True)
-    if "ari" not in combined.columns:
-        combined["ari"] = np.nan
-
-    aggregated = (
-        combined.groupby("min_cluster_size")
-        .agg(
-            {
-                "dbcv": ["mean", "std", "count"],
-                "icm": "mean",
-                "n_clusters": "mean",
-                "ari": ["mean", "std", "count"],
-            }
-        )
-        .reset_index()
-    )
-
-    aggregated.columns = [
-        "min_cluster_size",
-        "dbcv_mean",
-        "dbcv_std",
-        "dbcv_count",
-        "icm_mean",
-        "n_clusters_mean",
-        "ari_mean",
-        "ari_std",
-        "ari_count",
-    ]
-
-    aggregated["dbcv_std"] = aggregated["dbcv_std"].fillna(0.0)
-    aggregated["dbcv_count"] = aggregated["dbcv_count"].astype(int)
-    aggregated["ari_std"] = aggregated["ari_std"].fillna(0.0)
-    aggregated["ari_count"] = aggregated["ari_count"].astype(int)
-
-    points: list[AggregatedGridPoint] = []
-    for _, row in aggregated.sort_values("min_cluster_size").iterrows():
-        mcs = int(row["min_cluster_size"])
-        points.append(
-            AggregatedGridPoint(
-                min_cluster_size=mcs,
-                dbcv=ScalarMetricAggregate(
-                    mean=float(row["dbcv_mean"]),
-                    std=float(row["dbcv_std"]),
-                    count=int(row["dbcv_count"]),
-                ),
-                icm_mean=float(row["icm_mean"]),
-                n_clusters_mean=float(row["n_clusters_mean"]),
-                ari=ScalarMetricAggregate(
-                    mean=float(row["ari_mean"]),
-                    std=float(row["ari_std"]),
-                    count=int(row["ari_count"]),
-                ),
-            )
-        )
-
-    return AggregatedGridReport(points=tuple(points))
diff --git a/run/obsolete/analysis/__init__.py b/pelinker/core/__init__.py
similarity index 100%
rename from run/obsolete/analysis/__init__.py
rename to pelinker/core/__init__.py
diff --git a/pelinker/config.py b/pelinker/core/config.py
similarity index 89%
rename from pelinker/config.py
rename to pelinker/core/config.py
index 3b9757f..3345640 100644
--- a/pelinker/config.py
+++ b/pelinker/core/config.py
@@ -8,23 +8,21 @@
 
 from numpy.random import RandomState
 
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.scaling import ScaleCurve
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.kb.classes import ClassView
+from pelinker.core.scaling import ScaleCurve
 
 GridObjectiveSpec = Literal[
     "dbcv",
     "ari",
-    "dbcv_ari_mean_minmax",
-    "dbcv_ari_mean_raw",
+    "dbcv_ari_geomean",
 ]
 
 ScreenerKind = Literal["lda", "svm"]
 ManifoldKind = Literal["parametric", "umap"]
 PredictMode = Literal["compact", "legacy"]
 
-_GRID_OBJECTIVES: frozenset[str] = frozenset(
-    ("dbcv", "ari", "dbcv_ari_mean_minmax", "dbcv_ari_mean_raw")
-)
+_GRID_OBJECTIVES: frozenset[str] = frozenset(("dbcv", "ari", "dbcv_ari_geomean"))
 
 
 def _validate_batch_size(batch_size: int) -> None:
@@ -172,7 +170,7 @@ class EmbeddingTrainingConfig:
     """Rows read per ``pandas.read_csv(..., chunksize=...)`` pass over the text table (I/O buffer only)."""
     encoder_batch_size: int = 200
     """How many table rows are encoded per transformer forward pass; lower if GPU memory is tight."""
-    nlp_model: str = "en_core_web_trf"
+    nlp_model: str = "en_core_web_lg"
     max_input_buffers: int | None = None
     """If set, stop after this many text-table read passes (each up to ``input_buffer_rows`` rows)."""
     negatives_per_positive: float = 0.0
@@ -319,6 +317,13 @@ class LinkerFitConfig:
     max_mentions_per_entity: int | None = None
     max_mentions_negative: int | None = None
     mention_cap_seed: int = 13
+    class_view: ClassView = "raw"
+    """Which classes agreement metrics (ARI) score clusters against — see
+    :mod:`pelinker.kb.classes`. ``raw`` is the matched label; ``reldir`` (canonical
+    relation + direction) treats every relation's voices alike and needs ``class_kb_path``.
+    """
+    class_kb_path: str | None = None
+    """Pairs KB (``*.pairs.csv``) the ``rel`` / ``reldir`` views are computed from."""
     ambient_screener: NegativeScreenerConfig = field(
         default_factory=NegativeScreenerConfig
     )
@@ -336,7 +341,7 @@ class LinkerFitConfig:
     clustering_sample_index: int = 0
     """Bootstrap index for the clustering subsample (same contract as model selection ``sample_idx``)."""
     diagnostics_sample_size: int = 20_000
-    """Max rows of :class:`~pelinker.reporting.LinkerFitDiagnostics` stored on the fit report."""
+    """Max rows of :class:`~pelinker.reports.schema.LinkerFitDiagnostics` stored on the fit report."""
     diagnostics_random_state: int = 0
     """Stratified subsample seed for training diagnostics."""
     scale_curve: ScaleCurve | None = None
@@ -344,7 +349,7 @@ class LinkerFitConfig:
 
     When set (and no explicit ``min_cluster_size`` was given), the hyperparameter is
     extrapolated to the realized manifold row count instead of being transferred verbatim
-    from whatever sample size selection happened to run at. See :mod:`pelinker.scaling`.
+    from whatever sample size selection happened to run at. See :mod:`pelinker.core.scaling`.
     """
     predict_mode: PredictMode = "compact"
     """``compact``: ParametricUMAP + MLP entity head (no shipped HDBSCAN). ``legacy``: UMAP + HDBSCAN ``approximate_predict``."""
@@ -375,6 +380,8 @@ def to_clustering_sample_config(self) -> ClusteringOptimizationConfig:
             max_mentions_per_entity=self.max_mentions_per_entity,
             max_mentions_negative=self.max_mentions_negative,
             mention_cap_seed=self.mention_cap_seed,
+            class_view=self.class_view,
+            class_kb_path=self.class_kb_path,
             ambient_screener=self.ambient_screener,
             projection_screener=self.projection_screener,
         )
@@ -433,16 +440,36 @@ class ClusteringOptimizationConfig:
     max_mentions_per_entity: int | None = None
     max_mentions_negative: int | None = None
     mention_cap_seed: int = 13
-    optimization_method: str = "mean"
-    """How to build the objective f(min_cluster_size) before smoothing (mean / lower_bound / weighted)."""
-    grid_objective: GridObjectiveSpec = "dbcv_ari_mean_minmax"
-    """Which scalar to optimize on the grid (single metric or pooled DBCV+ARI; see ``clustering_grid``)."""
+    class_view: ClassView = "raw"
+    """Which classes agreement metrics (ARI) score clusters against — see
+    :mod:`pelinker.kb.classes`. ``raw`` is the matched label; ``reldir`` (canonical
+    relation + direction) treats every relation's voices alike and needs ``class_kb_path``.
+    """
+    class_kb_path: str | None = None
+    """Pairs KB (``*.pairs.csv``) the ``rel`` / ``reldir`` views are computed from."""
+    grid_objective: GridObjectiveSpec = "dbcv_ari_geomean"
+    """Which scalar to optimize on the grid.
+
+    ``dbcv_ari_geomean`` is ``sqrt(max(dbcv, 0) * max(ari, 0))``: a conjunctive score whose
+    ranking is invariant to the (arbitrary, and very different) scales of DBCV and ARI.
+    """
     grid_smooth_window: int = 3
-    """Odd-length centered moving-average window for smoothing f(x). Even values are bumped up by one."""
-    grid_plateau_fraction: float = 0.92
-    """Plateau threshold on the **smoothed** curve: ``y_min + this * (y_max - y_min)`` (finite values only)."""
-    grid_derivative_rel_tol: float = 0.12
-    """|df/dx| below this times max|df/dx| counts as “derivative near zero” on the smoothed curve."""
+    """Odd-length centered moving-average window applied to each sample's curve. Even values are bumped up by one."""
+    grid_one_se_k: float = 1.0
+    """How many paired standard errors from the best grid point still count as a tie.
+
+    The chosen ``min_cluster_size`` is the largest one reachable through consecutive grid
+    points within ``k`` standard errors of the argmax. 0 disables the rule (pure argmax).
+    """
+    grid_one_se_contiguous: bool = True
+    """Require the tying grid points to form an unbroken run from the argmax."""
+    grid_one_se_min_samples: int = 6
+    """Minimum bootstrap samples before the one-SE rule is applied at all.
+
+    Below this, fall back to the plain argmax. A standard error estimated from three
+    numbers is mostly noise, and acting on it makes the choice *less* reproducible: on this
+    repo's grid exports the slide only starts paying for itself around 6-8 samples.
+    """
     grid_cluster_count_reward: float = 0.0
     """Weight on ``log(n_clusters / n_ref)`` added to the grid objective (0 = disabled)."""
     grid_n_entities: int | None = None
@@ -481,18 +508,16 @@ def __post_init__(self) -> None:
             max_mentions_negative=self.max_mentions_negative,
         )
         _validate_clustering_sample_rows(self.clustering_sample_rows)
-        if not self.optimization_method:
-            raise ValueError("optimization_method must be a non-empty string")
         if self.grid_objective not in _GRID_OBJECTIVES:
             raise ValueError(
                 f"grid_objective must be one of {sorted(_GRID_OBJECTIVES)}"
             )
         if self.grid_smooth_window < 1:
             raise ValueError("grid_smooth_window must be >= 1")
-        if not 0 < self.grid_plateau_fraction <= 1:
-            raise ValueError("grid_plateau_fraction must be in (0, 1]")
-        if self.grid_derivative_rel_tol <= 0:
-            raise ValueError("grid_derivative_rel_tol must be > 0")
+        if self.grid_one_se_k < 0:
+            raise ValueError("grid_one_se_k must be >= 0")
+        if self.grid_one_se_min_samples < 1:
+            raise ValueError("grid_one_se_min_samples must be >= 1")
         if self.grid_cluster_count_reward < 0:
             raise ValueError("grid_cluster_count_reward must be >= 0")
         if self.grid_n_entities is not None and self.grid_n_entities < 1:
diff --git a/pelinker/core/onto.py b/pelinker/core/onto.py
new file mode 100644
index 0000000..27af3de
--- /dev/null
+++ b/pelinker/core/onto.py
@@ -0,0 +1,116 @@
+import dataclasses
+import functools
+from enum import Enum
+from dataclass_wizard import JSONWizard
+import torch
+from collections.abc import Iterator
+
+MAX_LENGTH = 512
+
+
+class WordGrouping(Enum):
+    W1 = 1
+    W2 = 2
+    W3 = 3
+    W4 = 4
+
+
+class BaseDataclass(JSONWizard, JSONWizard.Meta):
+    key_transform_with_dump = "SNAKE"
+    skip_defaults = True
+
+
+NEGATIVE_LABEL = "__NEGATIVE__"
+
+
+@dataclasses.dataclass
+class SimplifiedToken(BaseDataclass):
+    ix: int
+    ix_end: int
+    text: str
+    lemma: str
+    tag: str
+    pos: str | None = None  # spaCy ``token.pos_``; ``None`` only for legacy payloads
+    is_stop: bool | None = None  # spaCy ``token.is_stop``
+    # Dependency parse, kept to label weak-supervision mentions; never a model feature.
+    i: int | None = None  # spaCy ``token.i`` within the parsed chunk
+    head_i: int | None = None  # ``token.head.i``
+    dep: str | None = None  # ``token.dep_``
+
+
+@dataclasses.dataclass
+class Expression(BaseDataclass):
+    tokens: list[SimplifiedToken]
+    itext: int | None = None  # index of document
+    ichunk: int | None = None  # index of document chunk
+    a: int | None = None  # index of the first character
+    b: int | None = None  # exclusive end index (Python slice semantics)
+
+    def __post_init__(self):
+        self.tokens = sorted(self.tokens, key=lambda x: x.ix)
+        self.a = self.tokens[0].ix
+        self.b = self.tokens[-1].ix_end
+
+
+@dataclasses.dataclass
+class MentionCandidate(BaseDataclass):
+    """Typed mention payload used by :class:`pelinker.model.Linker` predictions."""
+
+    mention: str
+    a: int | None
+    b: int | None
+    a_abs: int | None = None
+    b_abs: int | None = None
+    itext: int | None = None
+    ichunk: int | None = None
+    word_grouping: WordGrouping | None = None
+    lemma: str = ""
+
+
+@dataclasses.dataclass
+class ExpressionHolder(BaseDataclass):
+    tt: torch.Tensor
+    expressions: list[Expression]
+
+    def __post_init__(self):
+        if len(self.expressions) != self.tt.shape[0]:
+            raise ValueError(
+                "The number of expressions does not match the shape of the tensor"
+            )
+
+    def filter_on_lemmas(
+        self, tokens: list[SimplifiedToken]
+    ) -> list[tuple[Expression, torch.Tensor]]:
+        return list(self.iter_on_lemmas(tokens))
+
+    @functools.cached_property
+    def _lemma_index(self) -> dict[str, list[int]]:
+        """Joined lemma string -> expression indices, ascending.
+
+        Built on first lookup, so ``expressions`` must not change afterwards.
+        """
+        index: dict[str, list[int]] = {}
+        for i, expression in enumerate(self.expressions):
+            key = " ".join(token.lemma for token in expression.tokens)
+            index.setdefault(key, []).append(i)
+        return index
+
+    def iter_on_lemmas(
+        self, tokens: list[SimplifiedToken]
+    ) -> Iterator[tuple[Expression, torch.Tensor]]:
+        tokens_lemmatized = " ".join(e.lemma for e in tokens)
+        for i in self._lemma_index.get(tokens_lemmatized, ()):
+            yield self.expressions[i], self.tt[i]
+
+
+@dataclasses.dataclass
+class ExpressionHolderBatch(BaseDataclass):
+    expression_data: list[ExpressionHolder]
+    word_grouping: WordGrouping | None = None
+
+
+def _wg_for_property(prop: str) -> WordGrouping | None:
+    n = len(prop.split())
+    if n in (1, 2, 3, 4):
+        return WordGrouping(n)
+    return None
diff --git a/pelinker/core/paths.py b/pelinker/core/paths.py
new file mode 100644
index 0000000..05f7cbe
--- /dev/null
+++ b/pelinker/core/paths.py
@@ -0,0 +1,32 @@
+"""Path expansion for config and CLI inputs."""
+
+import os
+from pathlib import Path
+
+import click
+
+
+def expand_config_path(path: str | os.PathLike[str] | None) -> Path | None:
+    """Expand environment variables and ``~`` in config or CLI path strings."""
+    if path is None:
+        return None
+    return Path(os.path.expandvars(os.fspath(path))).expanduser()
+
+
+class ExpandedPath(click.Path):
+    """``click.Path`` that expands ``~`` and ``$VAR`` before validating.
+
+    Click checks the literal string it is given, and a shell is what normally turns ``~``
+    into a home directory. Anything that builds argv directly — an IDE run configuration,
+    a systemd unit, ``subprocess`` with a list — passes the tilde through untouched, so
+    ``--flag ~/data/x`` fails ``exists=True`` against a path that exists. On the writing
+    side it is worse than an error: an unexpanded output path silently creates a
+    directory literally named ``~`` in the working directory.
+    """
+
+    def convert(self, value, param, ctx):  # type: ignore[override]
+        if isinstance(value, (str, os.PathLike)):
+            expanded = expand_config_path(value)
+            if expanded is not None:
+                value = os.fspath(expanded)
+        return super().convert(value, param, ctx)
diff --git a/pelinker/core/runtime.py b/pelinker/core/runtime.py
new file mode 100644
index 0000000..bd45fab
--- /dev/null
+++ b/pelinker/core/runtime.py
@@ -0,0 +1,43 @@
+"""Native-library load order for the one place it matters.
+
+If TensorFlow is initialised first, importing ``triton.runtime`` afterwards segfaults the
+interpreter inside ``triton/knobs.py``. pelinker loads both — torch for the encoders,
+TensorFlow via ``tf-keras`` for ParametricUMAP — so the torch side must come first.
+
+This used to live in ``pelinker/__init__.py``, which made *every* import of anything under
+``pelinker`` drag in torch: over a thousand modules and seconds of startup for scripts
+that only read a CSV and call an HTTP API, and a hard crash under a debugger that had
+already loaded TensorFlow. TensorFlow only ever enters through
+``umap.parametric_umap``, so the ordering is enforced at those call sites instead.
+
+Call :func:`preload_torch_before_tensorflow` immediately before importing anything that
+pulls in TensorFlow.
+"""
+
+from __future__ import annotations
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+_PRELOADED = False
+
+
+def preload_torch_before_tensorflow() -> bool:
+    """Import torch (and its triton runtime) ahead of TensorFlow. Idempotent.
+
+    Returns True when torch is present, False when it is not installed — a torch build
+    without CUDA ships no triton, and neither is needed by the pure-dataclass modules, so
+    absence is not an error.
+    """
+    global _PRELOADED
+    if _PRELOADED:
+        return True
+    try:
+        import torch  # noqa: F401
+        import triton.runtime  # noqa: F401
+    except ImportError:
+        logger.debug("torch/triton unavailable; skipping the pre-TensorFlow import")
+        return False
+    _PRELOADED = True
+    return True
diff --git a/pelinker/scaling.py b/pelinker/core/scaling.py
similarity index 96%
rename from pelinker/scaling.py
rename to pelinker/core/scaling.py
index 04544ae..617645a 100644
--- a/pelinker/scaling.py
+++ b/pelinker/core/scaling.py
@@ -10,11 +10,10 @@
 means.
 
 The grid solver compounds this. :func:`
-~pelinker.clustering_grid.solve_optimal_min_cluster_size_from_aggregated` min-max
-normalizes each metric *within* the curve and takes the leftmost point of a plateau
-defined relative to that curve's own range — so the choice is driven by the *shape* of
-f(MCS) over a fixed absolute window. Change N, the curve shifts, the plateau moves, and
-the window does not.
+~pelinker.clustering.grid.solve_optimal_min_cluster_size` picks the largest MCS that is
+statistically tied with the best point *on the grid it was given* — so the choice is
+driven by the shape of f(MCS) over a fixed absolute window. Change N, the curve shifts,
+the tie region moves, and the window does not.
 
 Rather than assume a correction, this module **measures** one: evaluate the same search
 at several sample sizes ("rungs"), fit ``log(MCS*) ~ a + b·log(N)``, and extrapolate.
@@ -26,7 +25,7 @@
 absolute value transfers as-is and today's behaviour was right all along; ``0 < b < 1``
 (the expected regime) means it grows sublinearly. A **flat or non-monotone** curve with
 low :attr:`ScaleCurve.r_squared` is a real finding, not a failure: it usually means the
-plateau selector is pinned against the fixed ``[min_scale, max_scale)`` grid, and the
+selector is pinned against the fixed ``[min_scale, max_scale)`` grid, and the
 grid must be widened before any extrapolation from it means anything. Check
 :attr:`ScaleCurve.pinned_rungs` before trusting a fit.
 """
diff --git a/pelinker/io/__init__.py b/pelinker/data/__init__.py
similarity index 60%
rename from pelinker/io/__init__.py
rename to pelinker/data/__init__.py
index 49a0de6..85fafbe 100644
--- a/pelinker/io/__init__.py
+++ b/pelinker/data/__init__.py
@@ -3,7 +3,7 @@
 Supports Feather, Parquet, and CSV/TSV formats.
 """
 
-from pelinker.io.json_files import is_gzip_file_path, load_json_path
-from pelinker.io.reader import read_batches
+from pelinker.data.json_files import is_gzip_file_path, load_json_path
+from pelinker.data.reader import read_batches
 
 __all__ = ["is_gzip_file_path", "load_json_path", "read_batches"]
diff --git a/pelinker/data/frames.py b/pelinker/data/frames.py
new file mode 100644
index 0000000..47a6e36
--- /dev/null
+++ b/pelinker/data/frames.py
@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+
+import numpy as np
+import pandas as pd
+import torch
+
+
+"""Mention-frame shaping helpers shared by fit and search."""
+
+
+def drop_entities_with_few_mentions(
+    frame: pd.DataFrame,
+    min_mentions_per_entity: int,
+    *,
+    negative_label: str | None = None,
+) -> pd.DataFrame:
+    """
+    Drop entities with fewer than ``min_mentions_per_entity`` rows (same rule as
+    :func:`~pelinker.search.selection.load_selection_frame` / mention-level selection eval).
+
+    When ``negative_label`` is set, that label is never dropped for low mention count
+    (so thin negative tails remain for screener training).
+    """
+    if "entity" not in frame.columns:
+        raise ValueError("frame must contain an 'entity' column")
+    mention_count = frame["entity"].value_counts()
+    low_count = mention_count[
+        ~(mention_count >= min_mentions_per_entity)
+    ].index.to_list()
+    if negative_label is not None:
+        low_count = [e for e in low_count if e != negative_label]
+    return frame.loc[~frame["entity"].isin(low_count)].copy()
+
+
+def embeddings_dict_to_dataframe(
+    embeddings_dict: dict[str, tuple[str, torch.Tensor | np.ndarray]],
+) -> pd.DataFrame:
+    """
+    Convert embeddings dictionary to DataFrame format expected by transform artifacts.
+
+    Args:
+        embeddings_dict: Dictionary mapping id -> (label, embedding)
+
+    Returns:
+        DataFrame with columns: id, label, embed
+    """
+    embeddings_list = []
+    id_list = []
+    label_list = []
+
+    for id_val, (label, emb) in embeddings_dict.items():
+        if isinstance(emb, torch.Tensor):
+            emb_np = emb.detach().cpu().numpy()
+        else:
+            emb_np = np.array(emb)
+        embeddings_list.append(emb_np)
+        id_list.append(id_val)
+        label_list.append(label)
+
+    return pd.DataFrame({"id": id_list, "label": label_list, "embed": embeddings_list})
+
+
+def entity_negative_label_mask_01(
+    entities: pd.Series | np.ndarray,
+    negative_label: str,
+) -> np.ndarray:
+    """
+    Per-row binary labels aligned with ``entities``: ``1`` if the row's ``entity`` equals
+    ``negative_label`` (same convention as the negative screener positive class), else ``0``.
+    """
+    if isinstance(entities, pd.Series):
+        s = entities.astype(str).to_numpy()
+    else:
+        s = np.asarray(entities).astype(str)
+    if s.size == 0:
+        return np.zeros(0, dtype=np.int64)
+    return (s == negative_label).astype(np.int64, copy=False)
+
+
+def mention_quality_frame(
+    dfr: pd.DataFrame,
+    *,
+    neg_mask: np.ndarray,
+    cluster_kb: np.ndarray,
+    pca_residuals: np.ndarray,
+    pca_mahalanobis: np.ndarray,
+    pca_spectral_entropy: np.ndarray,
+    negative_label: str,
+) -> pd.DataFrame:
+    """Per-mention PCA quality and labels for all rows (KB clustered; negatives cluster=-1)."""
+    optional = ["pmid", "mention"]
+    optional_cols = [c for c in optional if c in dfr.columns]
+    out = dfr[["entity", *optional_cols]].copy()
+    cluster_full = np.full(len(dfr), -1, dtype=np.int64)
+    cluster_full[~neg_mask] = np.asarray(cluster_kb, dtype=np.int64).ravel()
+    out["cluster"] = cluster_full
+    out["oov_label"] = entity_negative_label_mask_01(dfr["entity"], negative_label)
+    out["pca_residual"] = np.asarray(pca_residuals, dtype=np.float64).ravel()
+    out["pca_mahalanobis"] = np.asarray(pca_mahalanobis, dtype=np.float64).ravel()
+    out["pca_spectral_entropy"] = np.asarray(
+        pca_spectral_entropy, dtype=np.float64
+    ).ravel()
+    ordered = [
+        "entity",
+        *optional_cols,
+        "cluster",
+        "oov_label",
+        "pca_residual",
+        "pca_mahalanobis",
+        "pca_spectral_entropy",
+    ]
+    return out[ordered]
diff --git a/pelinker/embedding_fusion.py b/pelinker/data/fusion.py
similarity index 99%
rename from pelinker/embedding_fusion.py
rename to pelinker/data/fusion.py
index fab3202..7d7be1b 100644
--- a/pelinker/embedding_fusion.py
+++ b/pelinker/data/fusion.py
@@ -20,7 +20,7 @@
 import pandas as pd
 import pyarrow.parquet as pq
 
-from pelinker.io import read_batches
+from pelinker.data import read_batches
 
 logger = logging.getLogger(__name__)
 
@@ -285,7 +285,7 @@ def concat_mention_level_embedding_sources(
 ) -> pd.DataFrame | None:
     """
     Load one or more mention-level parquet sources like
-    :func:`~pelinker.selection.load_selection_frame`:
+    :func:`~pelinker.search.selection.load_selection_frame`:
     read each path in batches, optionally inner-join across sources, return one frame.
     """
     if len(paths) == 0:
diff --git a/pelinker/io/json_files.py b/pelinker/data/json_files.py
similarity index 100%
rename from pelinker/io/json_files.py
rename to pelinker/data/json_files.py
diff --git a/pelinker/io/parquet.py b/pelinker/data/parquet.py
similarity index 88%
rename from pelinker/io/parquet.py
rename to pelinker/data/parquet.py
index e26ba6e..f2c49bb 100644
--- a/pelinker/io/parquet.py
+++ b/pelinker/data/parquet.py
@@ -34,6 +34,10 @@ def _get_schema(self) -> pa.Schema:
             pa.field("ichunk", pa.int64()),
             # For embeddings, we'll use list of float64
             pa.field("embed", pa.list_(pa.float64())),
+            # How the weak label was assigned: parse-based voice for verb predicates
+            # (``direction`` forward|inverse|symmetric), ``lexical`` otherwise.
+            pa.field("direction", pa.string()),
+            pa.field("surface_rule", pa.string()),
         ]
         return pa.schema(fields)
 
@@ -53,6 +57,8 @@ def _dict_list_to_arrow_arrays(self, data: list[dict]) -> tuple:
         itext_vals = [row.get("itext") for row in data]
         ichunk_vals = [row.get("ichunk") for row in data]
         embeds = [row["embed"] for row in data]
+        directions = [row.get("direction") for row in data]
+        surface_rules = [row.get("surface_rule") for row in data]
 
         # Create PyArrow arrays directly with explicit types
         pmid_array = pa.array(pmids, type=pa.string())
@@ -65,6 +71,8 @@ def _dict_list_to_arrow_arrays(self, data: list[dict]) -> tuple:
         itext_array = pa.array(itext_vals, type=pa.int64())
         ichunk_array = pa.array(ichunk_vals, type=pa.int64())
         embed_array = pa.array(embeds, type=pa.list_(pa.float64()))
+        direction_array = pa.array(directions, type=pa.string())
+        surface_rule_array = pa.array(surface_rules, type=pa.string())
 
         return (
             pmid_array,
@@ -77,6 +85,8 @@ def _dict_list_to_arrow_arrays(self, data: list[dict]) -> tuple:
             itext_array,
             ichunk_array,
             embed_array,
+            direction_array,
+            surface_rule_array,
         )
 
     def _dict_list_to_arrow_table(self, data: list[dict]) -> pa.Table:
diff --git a/pelinker/io/reader.py b/pelinker/data/reader.py
similarity index 100%
rename from pelinker/io/reader.py
rename to pelinker/data/reader.py
diff --git a/pelinker/ops.py b/pelinker/data/tables.py
similarity index 100%
rename from pelinker/ops.py
rename to pelinker/data/tables.py
diff --git a/run/obsolete/experiments/__init__.py b/pelinker/embed/__init__.py
similarity index 100%
rename from run/obsolete/experiments/__init__.py
rename to pelinker/embed/__init__.py
diff --git a/pelinker/embedder.py b/pelinker/embed/corpus.py
similarity index 88%
rename from pelinker/embedder.py
rename to pelinker/embed/corpus.py
index 52c8d9d..94a772a 100644
--- a/pelinker/embedder.py
+++ b/pelinker/embed/corpus.py
@@ -1,6 +1,7 @@
 import os
 import pathlib
-from collections.abc import Sequence
+from collections.abc import Iterator, Sequence
+from typing import TypeVar
 
 import logging
 
@@ -17,19 +18,34 @@
     TimeElapsedColumn,
 )
 
-from pelinker.config import (
+from pelinker.core.config import (
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
     EmbeddingTrainingConfig,
 )
-from pelinker.ops import iter_pmid_text_table_chunks
-from pelinker.util import expand_config_path, extract_and_embed_mentions, load_models
-from pelinker.onto import WordGrouping
-from pelinker.util import str2layers
-from pelinker.io.parquet import ParquetWriter
+from pelinker.data.tables import iter_pmid_text_table_chunks
+from pelinker.core.paths import expand_config_path
+from pelinker.text.embed import extract_and_embed_mentions
+from pelinker.text.models import load_models
+from pelinker.core.onto import WordGrouping
+from pelinker.text.models import str2layers
+from pelinker.data.parquet import ParquetWriter
 
 logger = logging.getLogger(__name__)
 
+T = TypeVar("T")
+
+
+def prepend_first(first: T, rest: Iterator[T]) -> Iterator[T]:
+    """``first`` followed by the rest of an iterator it was taken from.
+
+    A module-level function, not a closure: a closure over the caller's iterator variable
+    sees it after it is rebound to this very generator, which then consumes itself at the
+    second item ("generator already executing").
+    """
+    yield first
+    yield from rest
+
 
 def _progress_disabled() -> bool:
     """Set ``PELINKER_NO_PROGRESS=1`` to turn off the embed progress bar (e.g. CI logs)."""
@@ -59,11 +75,19 @@ def _embed_corpus_single_source(
         model.to("cuda")
         spacy.require_gpu()
 
-    nlp = spacy.load(training.nlp_model)
+    # Entities are never read; NER is a large share of spaCy time.
+    nlp = spacy.load(training.nlp_model, exclude=["ner"])
     layers = str2layers(source.layers_spec)
 
     df_kb = pd.read_csv(training.kb_csv_path)
     entities = df_kb["label"].tolist()
+    # Symmetric relations are never given an inverse direction; the pairs KB carries the
+    # flag, a plain KB has none.
+    symmetric_labels = (
+        frozenset(df_kb.loc[df_kb["is_symmetric"].fillna(False).astype(bool), "label"])
+        if "is_symmetric" in df_kb.columns
+        else frozenset()
+    )
 
     logger.info("Loaded %s entities", df_kb.shape[0])
     logger.info("Layers are set to %s", layers)
@@ -79,12 +103,7 @@ def _embed_corpus_single_source(
     else:
         _chunk0, pmid_col, text_col = _first_chunk
         logger.info("Using columns: %r, %r", pmid_col, text_col)
-
-        def _chunk_stream():
-            yield _chunk0, pmid_col, text_col
-            yield from chunk_iter
-
-        chunk_iter = _chunk_stream()
+        chunk_iter = prepend_first(_first_chunk, chunk_iter)
 
     if training.max_input_buffers is not None:
         logger.info(
@@ -222,6 +241,7 @@ def on_encoder_batch(
                         random_seed=training.negative_seed,
                         negative_random_state=negative_sampler,
                         on_encoder_batch=on_encoder_batch,
+                        symmetric_labels=symmetric_labels,
                     )
 
                 if rows_data:
diff --git a/pelinker/eval/__init__.py b/pelinker/eval/__init__.py
new file mode 100644
index 0000000..be30900
--- /dev/null
+++ b/pelinker/eval/__init__.py
@@ -0,0 +1 @@
+"""Gold-set evaluation: harness (two scoring regimes) and reference baselines."""
diff --git a/pelinker/eval/baselines.py b/pelinker/eval/baselines.py
new file mode 100644
index 0000000..217d6fa
--- /dev/null
+++ b/pelinker/eval/baselines.py
@@ -0,0 +1,241 @@
+"""Reference baselines for the gold evaluation.
+
+All three baselines share one span proposer (:class:`LexicalSpanProposer`, lemma-window
+matching against KB labels — the same idea the linker's stage (A) uses), and differ only
+in how a span gets its id:
+
+- :class:`LexicalLemmaBaseline` — the trivial baseline: the id of the KB label whose
+  lemma sequence the span matches.
+- :class:`EncoderKnnBaseline` — embed the span's context window and the KB's
+  ``label: description`` strings with a sentence encoder; cosine top-1.
+- :class:`LlmLinkerBaseline` — ask an LLM to pick an id from the full KB for the marked
+  mention; disk-cached, so re-scoring is free. Configure a different model (ideally a
+  different family) from the one that pre-annotated the gold, or the comparison is
+  circular. Provider seam and credentials: :mod:`pelinker.eval.llm`.
+
+Heavy imports (spaCy models, sentence-transformers, the LLM SDK) happen at construction
+or call time, never at module import.
+"""
+
+from __future__ import annotations
+
+import pathlib
+import re
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from pelinker.eval.kb_prompt import render_kb_catalog
+from pelinker.eval.llm import DEFAULT_PROVIDER, complete
+
+_MAX_WINDOW = 4
+_FENCE_RE = re.compile(r"^```(?:json)?\s*|\s*```$", re.MULTILINE)
+
+
+def _label_records(kb: pd.DataFrame) -> list[tuple[str, str, str]]:
+    """(entity_id, label, description) rows with a usable label."""
+    out: list[tuple[str, str, str]] = []
+    for _, r in kb.iterrows():
+        label = r.get("label")
+        if pd.isna(label) or not str(label).strip():
+            continue
+        desc = "" if pd.isna(r.get("description")) else str(r["description"])
+        out.append((str(r["entity_id"]), str(label), desc))
+    return out
+
+
+class LexicalSpanProposer:
+    """Lemma-window span proposal over KB labels — shared by every baseline's e2e run."""
+
+    def __init__(self, kb: pd.DataFrame, nlp: Any) -> None:
+        self._nlp = nlp
+        self._index: dict[tuple[str, ...], str] = {}
+        # Sorted so a duplicated label resolves to the smallest id, deterministically.
+        for entity_id, label, _ in sorted(_label_records(kb)):
+            doc = nlp(label)
+            lemmas = tuple(
+                t.lemma_.lower() for t in doc if not (t.is_punct or t.is_space)
+            )
+            if 0 < len(lemmas) <= _MAX_WINDOW:
+                self._index.setdefault(lemmas, entity_id)
+
+    def lookup_lemmas(self, lemmas: tuple[str, ...]) -> str | None:
+        return self._index.get(lemmas)
+
+    def propose(self, text: str) -> list[tuple[int, int, str]]:
+        """Non-overlapping ``(a, b, entity_id)`` spans, longest match first."""
+        doc = self._nlp(text)
+        tokens = [t for t in doc if not (t.is_punct or t.is_space)]
+        lemmas = [t.lemma_.lower() for t in tokens]
+        taken: set[int] = set()
+        spans: list[tuple[int, int, str]] = []
+        for width in range(_MAX_WINDOW, 0, -1):
+            for i in range(len(tokens) - width + 1):
+                if any(j in taken for j in range(i, i + width)):
+                    continue
+                entity_id = self._index.get(tuple(lemmas[i : i + width]))
+                if entity_id is None:
+                    continue
+                a = tokens[i].idx
+                b = tokens[i + width - 1].idx + len(tokens[i + width - 1])
+                spans.append((a, b, entity_id))
+                taken.update(range(i, i + width))
+        return sorted(spans)
+
+    def predict(self, texts: list[str]) -> list[dict[str, Any]]:
+        """End-to-end prediction rows in the scorer's shape."""
+        rows: list[dict[str, Any]] = []
+        for itext, text in enumerate(texts):
+            for a, b, entity_id in self.propose(text):
+                rows.append(
+                    {"itext": itext, "a": a, "b": b, "entity_id_predicted": entity_id}
+                )
+        return rows
+
+
+class LexicalLemmaBaseline:
+    """Trivial baseline: the span's id is the id of the lemma-matched KB label."""
+
+    def __init__(self, kb: pd.DataFrame, nlp: Any) -> None:
+        self._proposer = LexicalSpanProposer(kb, nlp)
+        self._nlp = nlp
+
+    @property
+    def proposer(self) -> LexicalSpanProposer:
+        return self._proposer
+
+    def link(self, text: str, a: int, b: int) -> str | None:
+        """Id for a given span: exact lemma match, else the longest matching sub-window.
+
+        The back-off matters for fairness. Gold spans are annotated as whole predicate
+        phrases ("is activated by") while KB labels are often the bare form
+        ("activated"), so an exact-only baseline would abstain on precisely the mentions
+        the task is about — making the comparison a strawman rather than a floor.
+        """
+        doc = self._nlp(text[a:b])
+        lemmas = [t.lemma_.lower() for t in doc if not (t.is_punct or t.is_space)]
+        exact = self._proposer.lookup_lemmas(tuple(lemmas))
+        if exact is not None:
+            return exact
+        for width in range(min(len(lemmas), _MAX_WINDOW), 0, -1):
+            for i in range(len(lemmas) - width + 1):
+                hit = self._proposer.lookup_lemmas(tuple(lemmas[i : i + width]))
+                if hit is not None:
+                    return hit
+        return None
+
+    def predict(self, texts: list[str]) -> list[dict[str, Any]]:
+        return self._proposer.predict(texts)
+
+
+class EncoderKnnBaseline:
+    """Sentence-encoder cosine top-1 of the mention against the KB label strings.
+
+    Two defaults are deliberate, and both were chosen by sweeping them on gold rather
+    than assumed:
+
+    - ``context_chars=0`` — the mention is embedded alone. Padding it with surrounding
+      sentence text makes the vector a function of the *sentence* rather than the
+      predicate: adjacent mentions then collapse onto the same prediction, and accuracy
+      falls off a cliff as the window grows.
+    - ``include_description=False`` — labels are matched bare. A KB description is prose
+      about the relation, and concatenating it pulls the label vector away from the
+      surface form the mention actually resembles.
+
+    Getting these wrong turns a competitive baseline into a strawman, which is precisely
+    the failure the baseline exists to rule out.
+    """
+
+    def __init__(
+        self,
+        kb: pd.DataFrame,
+        *,
+        model_name: str = "neuml/pubmedbert-base-embeddings",
+        context_chars: int = 0,
+        include_description: bool = False,
+        min_similarity: float | None = None,
+    ) -> None:
+        from sentence_transformers import SentenceTransformer
+
+        self._context_chars = context_chars
+        self._min_similarity = min_similarity
+        records = _label_records(kb)
+        self._entity_ids = [eid for eid, _, _ in records]
+        self._model = SentenceTransformer(model_name)
+        label_texts = [
+            f"{label}: {desc}" if (include_description and desc) else label
+            for _, label, desc in records
+        ]
+        kb_matrix = np.asarray(
+            self._model.encode(label_texts, batch_size=32, show_progress_bar=False)
+        )
+        self._kb_matrix = kb_matrix / np.linalg.norm(kb_matrix, axis=1, keepdims=True)
+
+    def _context(self, text: str, a: int, b: int) -> str:
+        lo = max(0, a - self._context_chars)
+        hi = min(len(text), b + self._context_chars)
+        return text[lo:hi]
+
+    def link(self, text: str, a: int, b: int) -> str | None:
+        vec = np.asarray(
+            self._model.encode([self._context(text, a, b)], show_progress_bar=False)
+        )[0]
+        vec = vec / np.linalg.norm(vec)
+        sims = self._kb_matrix @ vec
+        best = int(np.argmax(sims))
+        if self._min_similarity is not None and sims[best] < self._min_similarity:
+            return None
+        return self._entity_ids[best]
+
+
+class LlmLinkerBaseline:
+    """LLM constrained choice over the KB for a marked mention; disk-cached."""
+
+    _SYSTEM_TEMPLATE = (
+        "You link predicate mentions in biomedical text to a fixed list of relations.\n\n"
+        "Relations (numbered; label in quotes):\n\n"
+        "{kb_table}\n\n"
+        "The user message contains a passage with ONE mention marked between 【 and 】. "
+        "Reply with the single best-matching relation label, copied exactly and without "
+        "quotes, or NONE if no relation fits. Reply with only that label."
+    )
+
+    def __init__(
+        self,
+        kb: pd.DataFrame,
+        *,
+        model: str,
+        cache_dir: pathlib.Path | str,
+        provider: str = DEFAULT_PROVIDER,
+        context_chars: int = 240,
+    ) -> None:
+        records = _label_records(kb)
+        # Labels, not ids, and rendered through the same cleaner the annotator uses: a
+        # baseline handicapped by a messier prompt than the system it is compared with
+        # is not a baseline, it is a strawman.
+        self._label_index = {label.strip().casefold(): eid for eid, label, _ in records}
+        self._system = self._SYSTEM_TEMPLATE.replace(
+            "{kb_table}", render_kb_catalog(kb)
+        )
+        self._model_name = model
+        self._provider = provider
+        self._cache_dir = pathlib.Path(cache_dir)
+        self._context_chars = context_chars
+
+    def link(self, text: str, a: int, b: int) -> str | None:
+        lo = max(0, a - self._context_chars)
+        hi = min(len(text), b + self._context_chars)
+        marked = text[lo:a] + "【" + text[a:b] + "】" + text[b:hi]
+        raw = complete(
+            system=self._system,
+            user=marked,
+            cache_dir=self._cache_dir,
+            provider=self._provider,
+            model=self._model_name,
+            max_output_tokens=64,
+        )
+        answer = _FENCE_RE.sub("", raw.strip()).strip().strip('"')
+        if not answer or answer.upper() == "NONE":
+            return None
+        return self._label_index.get(answer.casefold())
diff --git a/pelinker/eval/harness.py b/pelinker/eval/harness.py
new file mode 100644
index 0000000..e295b4e
--- /dev/null
+++ b/pelinker/eval/harness.py
@@ -0,0 +1,216 @@
+"""One scoring path for the linker and every baseline, over gold-annotated documents.
+
+Two regimes, deliberately separated:
+
+- **End-to-end** — the system proposes spans *and* ids on raw text; scored with
+  :func:`pelinker.kb.ground_truth.score_predictions_against_ground_truth` (span
+  detection P/R/F1 + entity accuracy over matched spans).
+- **Linking-only** — gold spans are given and the system only assigns an id per span.
+  This isolates the discriminative comparison from span proposal, which for the linker
+  (and the reference baselines) is lemma matching either way.
+
+Every evaluation records wall-clock time and document count, so runtime tables come from
+the same run as the quality numbers.
+"""
+
+from __future__ import annotations
+
+import json
+import pathlib
+import time
+from dataclasses import dataclass
+from typing import Any, Callable, Mapping, Sequence
+
+from pelinker.kb.ground_truth import (
+    GroundTruthScore,
+    GtSpan,
+    load_ground_truth_spans,
+    score_predictions_against_ground_truth,
+)
+
+
+@dataclass(frozen=True)
+class GoldDoc:
+    """One gold-annotated document: text plus its spans (``itext`` already set)."""
+
+    doc_id: int
+    text: str
+    spans: tuple[GtSpan, ...]
+
+
+def load_gold_docs(path: pathlib.Path | str) -> list[GoldDoc]:
+    """Read the document-list gold JSON, keeping text and spans grouped per document.
+
+    ``itext`` on the returned spans is the document's position in the file — the same
+    convention :func:`load_ground_truth_spans` applies — so predictions indexed by
+    position in the returned list line up.
+    """
+    p = pathlib.Path(path).expanduser()
+    data = json.loads(p.read_text(encoding="utf-8"))
+    items = data if isinstance(data, list) else [data]
+    spans_by_doc: dict[int, list[GtSpan]] = {}
+    for span in load_ground_truth_spans(p):
+        spans_by_doc.setdefault(span.itext, []).append(span)
+    docs: list[GoldDoc] = []
+    for itext, item in enumerate(items):
+        docs.append(
+            GoldDoc(
+                doc_id=int(item.get("doc_id", itext)),
+                text=str(item["text"]),
+                spans=tuple(spans_by_doc.get(itext, [])),
+            )
+        )
+    return docs
+
+
+PredictFn = Callable[[list[str]], Sequence[Mapping[str, Any]]]
+"""Texts → prediction rows (``itext``/``a``/``b``/``entity_id_predicted``)."""
+
+LinkFn = Callable[[str, int, int], str | None]
+"""(text, a, b) → predicted entity id, or ``None`` to abstain."""
+
+
+@dataclass(frozen=True)
+class EvalRun:
+    """A scored regime plus its runtime, ready for a results table."""
+
+    system: str
+    regime: str
+    n_docs: int
+    wall_seconds: float
+    score: dict[str, Any]
+
+    def to_jsonable(self) -> dict[str, Any]:
+        return {
+            "system": self.system,
+            "regime": self.regime,
+            "n_docs": self.n_docs,
+            "wall_seconds": round(self.wall_seconds, 3),
+            **self.score,
+        }
+
+
+def evaluate_end_to_end(
+    predict_fn: PredictFn,
+    docs: Sequence[GoldDoc],
+    *,
+    system: str,
+    match_mode: str = "overlap",
+    predicted_id_to_kb_in: Mapping[str, str] | None = None,
+    canonicalize: Mapping[str, str] | None = None,
+) -> EvalRun:
+    """Span+link scoring of a full predictor over the gold documents.
+
+    ``canonicalize`` folds converse pairs onto the canonical space gold is annotated in;
+    see :func:`pelinker.eval.kb.canonical_id_map`.
+    """
+    gold = [span for doc in docs for span in doc.spans]
+    t0 = time.perf_counter()
+    predictions = predict_fn([doc.text for doc in docs])
+    wall = time.perf_counter() - t0
+    score: GroundTruthScore = score_predictions_against_ground_truth(
+        list(predictions),
+        gold,
+        match_mode=match_mode,
+        predicted_id_to_kb_in=predicted_id_to_kb_in,
+        canonicalize=canonicalize,
+    )
+    return EvalRun(
+        system=system,
+        regime="end_to_end",
+        n_docs=len(docs),
+        wall_seconds=wall,
+        score=score.to_jsonable(),
+    )
+
+
+@dataclass(frozen=True)
+class LinkingOnlyScore:
+    """Id assignment quality when spans are given."""
+
+    n_spans: int
+    n_predicted: int
+    n_correct: int
+
+    @property
+    def coverage(self) -> float | None:
+        """Fraction of gold spans the system committed an id to (1 − abstention)."""
+        if self.n_spans == 0:
+            return None
+        return self.n_predicted / self.n_spans
+
+    @property
+    def accuracy_on_predicted(self) -> float | None:
+        if self.n_predicted == 0:
+            return None
+        return self.n_correct / self.n_predicted
+
+    @property
+    def accuracy_overall(self) -> float | None:
+        """Abstentions count as wrong — the honest headline number."""
+        if self.n_spans == 0:
+            return None
+        return self.n_correct / self.n_spans
+
+    def to_jsonable(self) -> dict[str, Any]:
+        return {
+            "n_spans": self.n_spans,
+            "n_predicted": self.n_predicted,
+            "n_correct": self.n_correct,
+            "coverage": self.coverage,
+            "accuracy_on_predicted": self.accuracy_on_predicted,
+            "accuracy_overall": self.accuracy_overall,
+        }
+
+
+def evaluate_linking_only(
+    link_fn: LinkFn,
+    docs: Sequence[GoldDoc],
+    *,
+    system: str,
+    predicted_id_to_kb_in: Mapping[str, str] | None = None,
+    canonicalize: Mapping[str, str] | None = None,
+) -> EvalRun:
+    """Score id assignment over the gold spans that carry an entity id.
+
+    Ids are compared in the same space as the end-to-end regime: mapped onto input-KB ids
+    when the system emits minted ones, then folded onto the canonical member of each
+    converse pair. Direction is not scored here — the reference baselines assign an id and
+    nothing else, so orientation belongs to the gold's own directionality analysis.
+    """
+    n_spans = n_predicted = n_correct = 0
+    t0 = time.perf_counter()
+    for doc in docs:
+        for span in doc.spans:
+            if span.entity_id is None:
+                continue
+            n_spans += 1
+            predicted = link_fn(doc.text, span.a, span.b)
+            if predicted is None:
+                continue
+            pred_id = str(predicted)
+            if predicted_id_to_kb_in is not None:
+                mapped = predicted_id_to_kb_in.get(pred_id)
+                if mapped is None:
+                    # Unmappable id: not comparable, so it counts as neither a hit nor a
+                    # miss — the same convention the end-to-end scorer uses.
+                    continue
+                pred_id = str(mapped)
+            n_predicted += 1
+            gold_id = span.entity_id
+            if canonicalize is not None:
+                pred_id = str(canonicalize.get(pred_id, pred_id))
+                gold_id = str(canonicalize.get(gold_id, gold_id))
+            if pred_id == gold_id:
+                n_correct += 1
+    wall = time.perf_counter() - t0
+    score = LinkingOnlyScore(
+        n_spans=n_spans, n_predicted=n_predicted, n_correct=n_correct
+    )
+    return EvalRun(
+        system=system,
+        regime="linking_only",
+        n_docs=len(docs),
+        wall_seconds=wall,
+        score=score.to_jsonable(),
+    )
diff --git a/pelinker/eval/kb.py b/pelinker/eval/kb.py
new file mode 100644
index 0000000..0c26957
--- /dev/null
+++ b/pelinker/eval/kb.py
@@ -0,0 +1,67 @@
+"""The canonical view of the property KB, shared by annotation and scoring.
+
+The KB carries both members of many converse pairs ("regulates" / "regulated by") as
+separate entries. ``run/preprocessing/derive_inverse_pairs.py`` links them and elects one
+member of each pair as canonical, adding ``is_canonical`` and ``canonical_entity_id``.
+
+Two consumers depend on that view, and they must agree or the measurement is silently
+wrong:
+
+- **Annotation** offers canonical labels only, carrying orientation in ``direction``, so a
+  passive mention has exactly one valid encoding.
+- **Scoring** therefore compares ids in canonical space. A system that answers "regulated
+  by" where gold says ("regulates", inverse) picked the right relation; scoring it wrong
+  would measure the vocabulary's redundancy rather than the system.
+
+A KB without those columns predates the derivation. Both consumers refuse it rather than
+falling back, because the fallback changes what a number means without changing its name.
+
+The id-level helpers live in :mod:`pelinker.kb.classes`, because training uses the same
+view; they are re-exported here for the evaluation code.
+"""
+
+from __future__ import annotations
+
+import pathlib
+
+import pandas as pd
+
+from pelinker.kb.classes import (
+    CANONICAL_COLUMNS,
+    canonical_id_map,
+    require_canonical_kb,
+    symmetric_ids,
+)
+
+__all__ = [
+    "CANONICAL_COLUMNS",
+    "canonical_id_map",
+    "canonical_kb",
+    "pending_review_pairs",
+    "require_canonical_kb",
+    "symmetric_ids",
+]
+
+
+def canonical_kb(
+    kb: pd.DataFrame, *, kb_csv_path: str | pathlib.Path | None = None
+) -> pd.DataFrame:
+    """Only the canonical member of each converse pair.
+
+    Offering both members would give a passive mention two equally valid encodings — the
+    ambiguity the canonical vocabulary exists to remove.
+    """
+    require_canonical_kb(kb, kb_csv_path=kb_csv_path)
+    return kb.loc[kb["is_canonical"].astype(bool)]
+
+
+def pending_review_pairs(kb: pd.DataFrame) -> pd.DataFrame:
+    """Rows whose converse pair came from a heuristic and is not curator-reviewed yet.
+
+    A wrong heuristic pair merges two distinct relations into one canonical id, which is
+    invisible downstream: gold, prompts and scores all agree with each other and are all
+    wrong together. Callers warn on a non-empty result.
+    """
+    if "needs_review" not in kb.columns:
+        return kb.iloc[0:0]
+    return kb.loc[kb["needs_review"].astype(bool)]
diff --git a/pelinker/eval/kb_prompt.py b/pelinker/eval/kb_prompt.py
new file mode 100644
index 0000000..0214e80
--- /dev/null
+++ b/pelinker/eval/kb_prompt.py
@@ -0,0 +1,96 @@
+"""Rendering the property KB into a prompt catalog.
+
+The KB descriptions come from OBO sources and are not prose written for a reader. Left
+raw they carry four things that hurt an annotation prompt, in rough order of severity:
+
+1. **Embedded newlines.** A description laid out over several lines silently turns one
+   relation into several entries, so the model is shown fragments like ``. X part_of Y``
+   as if they were relations to choose from.
+2. **Pure cross-references.** ``inverse of develops from``,
+   ``[copied from inverse property 'occurs in']`` — no definitional content, and they
+   introduce a second, unrelated notion of "inverse" next to the converse field.
+3. **Formal restatements.** ``Formally: x precedes y iff ω(x) <= α(y) ∧ …`` — Greek
+   symbols and quantifier notation that say nothing about how the relation is *worded* in
+   a sentence, which is the only thing the annotator has to judge.
+4. **Boilerplate**: source URLs, indentation, trailing whitespace.
+
+Symmetric relations are marked ``symmetric`` on their line, so the model is told — not
+left to guess — that orientation does not apply to them.
+
+Entries are numbered and labels quoted so each is a discrete, unambiguously delimited
+item — most labels contain spaces ("has part", "is concretized as"), and an unquoted
+label in a dash-joined line has no visible boundary.
+"""
+
+from __future__ import annotations
+
+import re
+
+import pandas as pd
+
+_WHITESPACE_RE = re.compile(r"\s+")
+_LEADING_BRACKET_RE = re.compile(r"^\s*\[[^\]]*\]\s*")
+_URL_RE = re.compile(r"https?://\S+")
+_FORMAL_RE = re.compile(r"\s*\bFormally\b\s*[:,].*$", re.IGNORECASE | re.DOTALL)
+_XREF_ONLY_RE = re.compile(r"^inverse\s+(of|property)\b", re.IGNORECASE)
+
+MAX_DESCRIPTION_CHARS = 240
+
+
+def clean_description(raw: object, *, max_chars: int = MAX_DESCRIPTION_CHARS) -> str:
+    """Normalize one KB description into a single-line definition, or ``""``.
+
+    Returns an empty string when nothing definitional survives — a cross-reference to
+    another relation tells the annotator nothing the converse field does not already say.
+    """
+    if raw is None or (isinstance(raw, float) and pd.isna(raw)):
+        return ""
+    text = str(raw)
+    text = _LEADING_BRACKET_RE.sub("", text)
+    text = _URL_RE.sub("", text)
+    text = _FORMAL_RE.sub("", text)
+    text = _WHITESPACE_RE.sub(" ", text).strip()
+    if not text or _XREF_ONLY_RE.match(text):
+        return ""
+    if len(text) > max_chars:
+        cut = text[:max_chars].rsplit(" ", 1)[0].rstrip(" ,;:.")
+        text = f"{cut}…"
+    return text
+
+
+def render_kb_catalog(
+    kb: pd.DataFrame,
+    *,
+    include_converse: bool = True,
+    max_chars: int = MAX_DESCRIPTION_CHARS,
+) -> str:
+    """Numbered catalog, exactly one line per relation.
+
+    One line per entry is a contract, not a formatting preference: the count of lines has
+    to equal the count of relations, or the model is choosing from a list that does not
+    match the vocabulary the answers are validated against.
+    """
+    lines: list[str] = []
+    for position, (_, row) in enumerate(kb.iterrows(), start=1):
+        label = _WHITESPACE_RE.sub(" ", str(row["label"])).strip()
+        parts = [f'{position}. "{label}"']
+        description = clean_description(row.get("description"), max_chars=max_chars)
+        if description:
+            parts.append(f"def: {description}")
+        is_symmetric = row.get("is_symmetric")
+        if (
+            is_symmetric is not None
+            and not pd.isna(is_symmetric)
+            and bool(is_symmetric)
+        ):
+            parts.append("symmetric")
+        if include_converse:
+            converse = row.get("inverse_label")
+            if isinstance(converse, str) and converse.strip():
+                clean_converse = _WHITESPACE_RE.sub(" ", converse).strip()
+                parts.append(f'converse wording: "{clean_converse}"')
+        line = " | ".join(parts)
+        if "\n" in line:  # pragma: no cover - defended against, not expected
+            raise ValueError(f"catalog line {position} contains a newline: {line!r}")
+        lines.append(line)
+    return "\n".join(lines)
diff --git a/pelinker/eval/llm.py b/pelinker/eval/llm.py
new file mode 100644
index 0000000..01d9a9f
--- /dev/null
+++ b/pelinker/eval/llm.py
@@ -0,0 +1,324 @@
+"""Cached LLM calls for the gold pipeline, over a small provider seam.
+
+Only the evaluation path talks to an LLM — the linker itself never does. Two callers use
+this module: gold pre-annotation (``run/eval/annotate_llm.py``) and the LLM linking
+baseline (:class:`pelinker.eval.baselines.LlmLinkerBaseline`).
+
+Design points that matter for the measurement:
+
+- **Responses are disk-cached** keyed on ``(provider, model, system, user, json_mode)``
+  (plus ``reasoning_effort`` when one is set), so re-runs, parser fixes and re-scoring
+  cost nothing and an annotation batch is reproducible from the cache alone.
+- **Only complete answers are cached.** A response the provider cut off at the output
+  limit raises :class:`LLMIncompleteError` instead. Cached, a truncated answer would
+  replay forever as an unparsable document with no candidates, and raising the limit
+  afterwards would not retry it. Reasoning models make this likely: their hidden
+  reasoning counts against the same output limit as the answer.
+- **Structured output is requested where the provider supports it** rather than parsed
+  out of prose, which removes a whole class of silent annotation loss.
+- **The provider is a parameter.** Gold annotated by one model and a baseline answered by
+  the same model would be a circular comparison, so the two must be configured to differ —
+  and the agreement slice is strongest when its second annotator is a different model
+  family, not just a different checkpoint.
+
+Providers and their credentials, all installed by the ``eval`` extra:
+
+============ ====================================== ==================================
+provider     credential                             structured output
+============ ====================================== ==================================
+``gemini``   ``GEMINI_API_KEY`` / ``GOOGLE_API_KEY`` response mime type (default)
+``anthropic`` ``ANTHROPIC_API_KEY``                 prompt-level, with prefix caching
+``openai``   ``OPENAI_API_KEY``                     prompt-level
+============ ====================================== ==================================
+
+Provider-enforced JSON is used only where it fits the contract. The annotation prompt
+answers with a top-level **array**, which the OpenAI and Anthropic JSON-object modes
+cannot express, so those two are asked for JSON in the prompt and parsed leniently rather
+than being handed a schema that would reshape the answer.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import pathlib
+from dataclasses import dataclass, field
+from typing import Any, Callable
+
+logger = logging.getLogger(__name__)
+
+PROVIDERS = ("gemini", "anthropic", "openai")
+
+DEFAULT_PROVIDER = "gemini"
+
+DEFAULT_MODEL = "gemini-3.1-flash-lite"
+"""Any current model id may be passed instead; nothing here depends on this one."""
+
+REASONING_PROVIDERS = ("openai",)
+"""Providers whose calls take a ``reasoning_effort``."""
+
+
+class LLMIncompleteError(RuntimeError):
+    """The provider stopped before the answer was complete (output limit, empty reply).
+
+    Never cached, so the same call is retried on the next run — typically with a larger
+    ``max_output_tokens``.
+    """
+
+
+@dataclass(frozen=True)
+class LLMResponse:
+    """Model text plus whatever usage the provider reported."""
+
+    text: str
+    model: str
+    provider: str
+    usage: dict[str, Any] = field(default_factory=dict)
+
+    def to_jsonable(self) -> dict[str, Any]:
+        return {
+            "provider": self.provider,
+            "model": self.model,
+            "text": self.text,
+            "usage": self.usage,
+        }
+
+
+def cache_key(
+    *,
+    provider: str,
+    model: str,
+    system: str,
+    user: str,
+    json_mode: bool,
+    reasoning_effort: str | None = None,
+) -> str:
+    fields: list[object] = [provider, model, system, user, json_mode]
+    # Appended only when set, so every key written before the parameter existed still
+    # resolves to the same cached answer.
+    if reasoning_effort is not None:
+        fields.append(reasoning_effort)
+    payload = json.dumps(fields, ensure_ascii=False)
+    return hashlib.sha256(payload.encode("utf-8")).hexdigest()[:32]
+
+
+def _complete_gemini(
+    *,
+    model: str,
+    system: str,
+    user: str,
+    max_output_tokens: int,
+    json_mode: bool,
+    reasoning_effort: str | None = None,
+) -> LLMResponse:
+    from google import genai
+    from google.genai import types
+
+    # Reads GEMINI_API_KEY / GOOGLE_API_KEY from the environment.
+    client = genai.Client()
+    config: dict[str, Any] = {
+        "system_instruction": system,
+        "max_output_tokens": max_output_tokens,
+        # Annotation and linking are extraction tasks, not generation: sampling noise
+        # here shows up as annotator disagreement that no one can adjudicate.
+        "temperature": 0.0,
+    }
+    if json_mode:
+        config["response_mime_type"] = "application/json"
+
+    response = client.models.generate_content(
+        model=model,
+        contents=user,
+        config=types.GenerateContentConfig(**config),
+    )
+    candidates = response.candidates or []
+    if candidates and candidates[0].finish_reason == types.FinishReason.MAX_TOKENS:
+        raise LLMIncompleteError(
+            f"gemini {model}: stopped at max_output_tokens={max_output_tokens}"
+        )
+    usage: dict[str, Any] = {}
+    meta = getattr(response, "usage_metadata", None)
+    if meta is not None:
+        for attr in (
+            "prompt_token_count",
+            "candidates_token_count",
+            "cached_content_token_count",
+            "total_token_count",
+        ):
+            value = getattr(meta, attr, None)
+            if value is not None:
+                usage[attr] = int(value)
+    return LLMResponse(
+        text=response.text or "",
+        model=model,
+        provider="gemini",
+        usage=usage,
+    )
+
+
+def _complete_anthropic(
+    *,
+    model: str,
+    system: str,
+    user: str,
+    max_output_tokens: int,
+    json_mode: bool,
+    reasoning_effort: str | None = None,
+) -> LLMResponse:
+    import anthropic
+
+    client = anthropic.Anthropic()
+    response = client.messages.create(
+        model=model,
+        max_tokens=max_output_tokens,
+        system=[
+            {
+                "type": "text",
+                "text": system,
+                # The KB table is a large prefix shared by every request in a batch.
+                "cache_control": {"type": "ephemeral"},
+            }
+        ],
+        messages=[{"role": "user", "content": user}],
+    )
+    if response.stop_reason == "max_tokens":
+        raise LLMIncompleteError(
+            f"anthropic {model}: stopped at max_tokens={max_output_tokens}"
+        )
+    text = "".join(block.text for block in response.content if block.type == "text")
+    return LLMResponse(
+        text=text,
+        model=model,
+        provider="anthropic",
+        usage={
+            "input_tokens": response.usage.input_tokens,
+            "output_tokens": response.usage.output_tokens,
+            "cache_read_input_tokens": getattr(
+                response.usage, "cache_read_input_tokens", None
+            ),
+        },
+    )
+
+
+def _complete_openai(
+    *,
+    model: str,
+    system: str,
+    user: str,
+    max_output_tokens: int,
+    json_mode: bool,
+    reasoning_effort: str | None = None,
+) -> LLMResponse:
+    from openai import OpenAI
+
+    # Reads OPENAI_API_KEY from the environment.
+    client = OpenAI()
+    # No temperature: the reasoning models reject the parameter outright, and the ones
+    # that accept it are already near-deterministic on an extraction prompt.
+    request: dict[str, Any] = {
+        "model": model,
+        "instructions": system,
+        "input": user,
+        "max_output_tokens": max_output_tokens,
+    }
+    if reasoning_effort is not None:
+        request["reasoning"] = {"effort": reasoning_effort}
+    response = client.responses.create(**request)
+    if response.status != "completed":
+        details = response.incomplete_details
+        reason = details.reason if details is not None else None
+        raise LLMIncompleteError(
+            f"openai {model}: status={response.status} reason={reason} "
+            f"(max_output_tokens={max_output_tokens}; reasoning tokens count against it)"
+        )
+    usage: dict[str, Any] = {}
+    reported = getattr(response, "usage", None)
+    if reported is not None:
+        for attr in ("input_tokens", "output_tokens", "total_tokens"):
+            value = getattr(reported, attr, None)
+            if value is not None:
+                usage[attr] = int(value)
+        details = getattr(reported, "input_tokens_details", None)
+        cached = getattr(details, "cached_tokens", None)
+        if cached is not None:
+            usage["cached_tokens"] = int(cached)
+        out_details = getattr(reported, "output_tokens_details", None)
+        reasoning = getattr(out_details, "reasoning_tokens", None)
+        if reasoning is not None:
+            usage["reasoning_tokens"] = int(reasoning)
+    return LLMResponse(
+        text=response.output_text or "",
+        model=model,
+        provider="openai",
+        usage=usage,
+    )
+
+
+def _completion_for(provider: str) -> Callable[..., LLMResponse]:
+    """The provider's call, resolved when it is needed.
+
+    Looked up per call rather than bound in a module-level table, so the module attribute
+    stays the single definition — a table built at import time would keep calling the
+    original function after the attribute is replaced.
+    """
+    return {
+        "gemini": _complete_gemini,
+        "anthropic": _complete_anthropic,
+        "openai": _complete_openai,
+    }[provider]
+
+
+def complete(
+    *,
+    system: str,
+    user: str,
+    cache_dir: pathlib.Path | str,
+    provider: str = DEFAULT_PROVIDER,
+    model: str = DEFAULT_MODEL,
+    max_output_tokens: int = 8000,
+    json_mode: bool = False,
+    reasoning_effort: str | None = None,
+) -> str:
+    """Return the model's text, calling the provider only on a cache miss.
+
+    Raises:
+        LLMIncompleteError: the provider cut the answer off or returned nothing; the
+            response is not cached.
+    """
+    if provider not in PROVIDERS:
+        raise ValueError(f"provider must be one of {PROVIDERS}, got {provider!r}")
+    if reasoning_effort is not None and provider not in REASONING_PROVIDERS:
+        raise ValueError(
+            f"reasoning_effort is supported for {REASONING_PROVIDERS}, not {provider!r}"
+        )
+
+    directory = pathlib.Path(cache_dir)
+    key = cache_key(
+        provider=provider,
+        model=model,
+        system=system,
+        user=user,
+        json_mode=json_mode,
+        reasoning_effort=reasoning_effort,
+    )
+    cache_file = directory / f"{key}.json"
+    if cache_file.exists():
+        return json.loads(cache_file.read_text(encoding="utf-8"))["text"]
+
+    response = _completion_for(provider)(
+        model=model,
+        system=system,
+        user=user,
+        max_output_tokens=max_output_tokens,
+        json_mode=json_mode,
+        reasoning_effort=reasoning_effort,
+    )
+    if not response.text.strip():
+        raise LLMIncompleteError(f"{provider} {model}: empty response")
+
+    directory.mkdir(parents=True, exist_ok=True)
+    cache_file.write_text(
+        json.dumps(response.to_jsonable(), ensure_ascii=False), encoding="utf-8"
+    )
+    return response.text
diff --git a/pelinker/eval/reference.py b/pelinker/eval/reference.py
new file mode 100644
index 0000000..d4eabee
--- /dev/null
+++ b/pelinker/eval/reference.py
@@ -0,0 +1,104 @@
+"""The reference inventory T̂: a scoring-only fold of the property KB.
+
+The KB is taken as given, so when two entries mean the same thing in text the KB keeps
+both. Scoring needs to know about such identities; training must not. T̂ carries them, in
+tiers:
+
+- **tier 0**, the KB's own declarations: each converse-pair member folds onto the pair's
+  canonical member (the same map the class views use, :func:`canonical_id_map`);
+- **tier 1** (``kb_implied``): identities the KB implies but does not declare, listed in an
+  equivalences file. For example, a converse minted for a verb that was later marked
+  symmetric is the same relation as that verb;
+- **tier 2**: synonymy and polysemy verdicts from blind adjudication, which join this
+  module with the revision sheet.
+
+T̂ is used for scoring only. Training labels come from :mod:`pelinker.kb.classes`, which
+accepts no judgement about the KB, so nothing here can reach the fit it is used to score.
+"""
+
+from __future__ import annotations
+
+import pathlib
+
+import pandas as pd
+
+from pelinker.kb.classes import canonical_id_map, symmetric_ids
+
+EQUIVALENCE_COLUMNS = ("entity_id", "equivalent_to", "tier", "note")
+TIER_KB_IMPLIED = "kb_implied"
+EQUIVALENCE_TIERS = (TIER_KB_IMPLIED,)
+
+
+def load_equivalences(
+    path: str | pathlib.Path, *, kb: pd.DataFrame | None = None
+) -> pd.DataFrame:
+    """Read an equivalences file (``entity_id,equivalent_to,tier,note``).
+
+    Each row says ``entity_id`` is the same relation as ``equivalent_to``. Rows are
+    refused, not skipped, when the tier is unknown, a row maps an id onto itself, an id
+    has two targets, or (given ``kb``) either id is not in the KB: a silently dropped
+    identity changes a score without changing its name.
+    """
+    frame = pd.read_csv(path, dtype=str).fillna("")
+    missing = [c for c in EQUIVALENCE_COLUMNS if c not in frame.columns]
+    if missing:
+        raise ValueError(f"{path}: missing columns {', '.join(missing)}")
+    frame = frame.assign(
+        entity_id=frame["entity_id"].str.strip(),
+        equivalent_to=frame["equivalent_to"].str.strip(),
+        tier=frame["tier"].str.strip(),
+    )
+    unknown = sorted(set(frame["tier"]) - set(EQUIVALENCE_TIERS))
+    if unknown:
+        raise ValueError(
+            f"{path}: unknown tier {unknown}; expected one of {EQUIVALENCE_TIERS}"
+        )
+    self_maps = frame.loc[frame["entity_id"] == frame["equivalent_to"], "entity_id"]
+    if not self_maps.empty:
+        raise ValueError(f"{path}: ids mapped onto themselves: {sorted(self_maps)}")
+    repeated = frame.loc[frame["entity_id"].duplicated(), "entity_id"]
+    if not repeated.empty:
+        raise ValueError(f"{path}: ids with more than one target: {sorted(repeated)}")
+    if kb is not None:
+        known = set(kb["entity_id"].astype(str))
+        unknown_ids = sorted(
+            (set(frame["entity_id"]) | set(frame["equivalent_to"])) - known
+        )
+        if unknown_ids:
+            raise ValueError(f"{path}: ids not in the KB: {unknown_ids}")
+    return frame
+
+
+def fold_map(
+    kb: pd.DataFrame, equivalences: pd.DataFrame | None = None
+) -> dict[str, str]:
+    """Every KB id → its T̂ class representative (tiers 0 and 1).
+
+    Tier 0 folds a converse member onto its canonical member; tier 1 then folds along the
+    equivalences, following chains to their end. A cycle is refused. Ids absent from the
+    map are left alone by callers, as with :func:`canonical_id_map`.
+    """
+    tier0 = canonical_id_map(kb)
+    tier1: dict[str, str] = {}
+    if equivalences is not None:
+        for source, target in zip(
+            equivalences["entity_id"], equivalences["equivalent_to"]
+        ):
+            tier1[tier0.get(source, source)] = tier0.get(target, target)
+
+    def _resolve(entity_id: str) -> str:
+        seen = {entity_id}
+        current = tier0.get(entity_id, entity_id)
+        while current in tier1:
+            current = tier1[current]
+            if current in seen:
+                raise ValueError(f"equivalence cycle through {entity_id!r}")
+            seen.add(current)
+        return current
+
+    return {entity_id: _resolve(entity_id) for entity_id in tier0}
+
+
+def folded_symmetric_ids(kb: pd.DataFrame, fold: dict[str, str]) -> frozenset[str]:
+    """Representatives of symmetric relations, where direction carries no information."""
+    return frozenset(fold.get(entity_id, entity_id) for entity_id in symmetric_ids(kb))
diff --git a/pelinker/eval/sample_quality.py b/pelinker/eval/sample_quality.py
new file mode 100644
index 0000000..8b0535f
--- /dev/null
+++ b/pelinker/eval/sample_quality.py
@@ -0,0 +1,175 @@
+"""Eligibility gate for the gold sample: which held-out abstracts may be drawn at all.
+
+A gold abstract has to be a real, English research abstract that the linker could in
+principle be scored on, and that the linker never saw in training. Without this gate a
+random draw lands on citation strings, news items, letters, truncated full texts and
+non-English records — documents that carry no predicate mentions and dilute every rate.
+
+Every check is a pure function of the text and metadata, and every dropped row carries the
+**first** reason it failed, so a sampling report can say how many documents each rule
+removed.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+import hashlib
+import re
+from collections.abc import Iterable
+from pathlib import Path
+
+import pandas as pd
+
+# Frequent English function words. Their share of word tokens separates English prose from
+# other languages without a language-identification dependency.
+ENGLISH_FUNCTION_WORDS = frozenset(
+    {
+        "the",
+        "of",
+        "and",
+        "in",
+        "to",
+        "a",
+        "is",
+        "was",
+        "were",
+        "are",
+        "with",
+        "for",
+        "that",
+        "by",
+        "we",
+        "this",
+        "on",
+        "as",
+        "from",
+        "these",
+        "which",
+        "be",
+        "an",
+        "or",
+    }
+)
+
+_WORD_RE = re.compile(r"[A-Za-z]+")
+_SENTENCE_END_RE = re.compile(r"[.!?](?:\s+|$)")
+
+REASON_YEAR = "year_before_min"
+REASON_SHORT = "too_short"
+REASON_LONG = "too_long"
+REASON_SENTENCES = "too_few_sentences"
+REASON_LANGUAGE = "not_english"
+REASON_DUPLICATE = "duplicate_text"
+REASON_TRAIN_OVERLAP = "in_training_corpus"
+
+
+@dataclasses.dataclass(frozen=True)
+class Eligibility:
+    """Thresholds of the gate; defaults describe a typical research abstract."""
+
+    min_year: int = 1990
+    min_chars: int = 400
+    max_chars: int = 3000
+    min_sentences: int = 3
+    min_english_share: float = 0.2
+
+
+def text_sha1(text: str) -> str:
+    return hashlib.sha1(text.encode("utf-8")).hexdigest()
+
+
+def english_share(text: str) -> float:
+    """Share of word tokens that are common English function words."""
+    words = [w.lower() for w in _WORD_RE.findall(text)]
+    if not words:
+        return 0.0
+    return sum(w in ENGLISH_FUNCTION_WORDS for w in words) / len(words)
+
+
+def count_sentences(text: str) -> int:
+    """Sentence-final punctuation followed by whitespace or the end of the text."""
+    return len(_SENTENCE_END_RE.findall(text.strip()))
+
+
+def drop_reason(text: str, year: object, rules: Eligibility) -> str | None:
+    """The first rule ``text`` fails, or ``None`` when it is eligible.
+
+    Duplicates and training overlap need the whole table; see :func:`apply_gate`.
+    """
+    try:
+        year_value = int(float(str(year)))
+    except ValueError:
+        return REASON_YEAR
+    if year_value < rules.min_year:
+        return REASON_YEAR
+    n = len(text)
+    if n < rules.min_chars:
+        return REASON_SHORT
+    if n > rules.max_chars:
+        return REASON_LONG
+    if count_sentences(text) < rules.min_sentences:
+        return REASON_SENTENCES
+    if english_share(text) < rules.min_english_share:
+        return REASON_LANGUAGE
+    return None
+
+
+@dataclasses.dataclass(frozen=True)
+class TrainingKeys:
+    """Identifiers of the fit corpus, for keeping gold documents out of training."""
+
+    mag: frozenset[str]
+    text_sha1: frozenset[str]
+
+
+def load_training_keys(
+    path: str | Path, *, id_col: int = 0, text_col: int = 1, chunksize: int = 100_000
+) -> TrainingKeys:
+    """Read the fit corpus (a TSV with an id column and a text column) in chunks."""
+    mags: set[str] = set()
+    hashes: set[str] = set()
+    for chunk in pd.read_csv(path, sep="\t", dtype=str, chunksize=chunksize):
+        ids = chunk.iloc[:, id_col].dropna().astype(str).str.strip()
+        mags.update(ids)
+        texts = chunk.iloc[:, text_col].fillna("").astype(str)
+        hashes.update(text_sha1(t) for t in texts)
+    return TrainingKeys(mag=frozenset(mags), text_sha1=frozenset(hashes))
+
+
+def apply_gate(
+    df: pd.DataFrame,
+    rules: Eligibility,
+    *,
+    text_col: str = "summary",
+    year_col: str = "publication_year",
+    mag_col: str = "mag",
+    training: TrainingKeys | None = None,
+) -> pd.Series:
+    """Per-row drop reason (``None`` = eligible), in rule order.
+
+    Row-level rules first, then training overlap, then exact duplicates among the rows
+    that are still eligible (the first occurrence is kept).
+    """
+    reasons = pd.Series(
+        [drop_reason(t, y, rules) for t, y in zip(df[text_col], df[year_col])],
+        index=df.index,
+        dtype=object,
+    )
+    hashes = df[text_col].map(text_sha1)
+    if training is not None:
+        mags = df[mag_col].fillna("").astype(str).str.strip()
+        overlap = mags.isin(training.mag) | hashes.isin(training.text_sha1)
+        reasons[reasons.isna() & overlap] = REASON_TRAIN_OVERLAP
+    alive = reasons.isna()
+    dup = hashes[alive].duplicated(keep="first")
+    reasons[dup[dup].index] = REASON_DUPLICATE
+    return reasons
+
+
+def reason_counts(reasons: Iterable[str | None]) -> dict[str, int]:
+    """``{reason: count}`` plus ``eligible``, for the sampling report."""
+    counts: dict[str, int] = {}
+    for r in reasons:
+        key = "eligible" if r is None or (isinstance(r, float) and pd.isna(r)) else r
+        counts[key] = counts.get(key, 0) + 1
+    return counts
diff --git a/pelinker/io/feather.py b/pelinker/io/feather.py
deleted file mode 100644
index fd02379..0000000
--- a/pelinker/io/feather.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from pelinker.io.reader import read_batches
-
-
-def read_feather_mmap_batches(file_path, batch_size=1000):
-    """
-    Read feather file using memory mapping for large files.
-
-    This is a convenience wrapper around the unified read_batches function.
-    For new code, prefer using pelinker.io.read_batches directly.
-    """
-    yield from read_batches(file_path, batch_size=batch_size, file_type="feather")
diff --git a/run/obsolete/preprocessing/__init__.py b/pelinker/kb/__init__.py
similarity index 100%
rename from run/obsolete/preprocessing/__init__.py
rename to pelinker/kb/__init__.py
diff --git a/pelinker/kb/classes.py b/pelinker/kb/classes.py
new file mode 100644
index 0000000..887b77c
--- /dev/null
+++ b/pelinker/kb/classes.py
@@ -0,0 +1,303 @@
+"""What counts as one class of the property KB, for training and for scoring.
+
+The KB is taken as given: its entries and labels are never edited here. What varies is
+the *view*, a deterministic projection computed from the KB's own declarations, namely
+the converse pairs and ``is_symmetric`` carried by the pairs KB that
+``run/preprocessing/derive_inverse_pairs.py`` writes:
+
+- ``raw``: the matched label itself. The two members of a converse pair are two classes.
+- ``rel``: the canonical relation. Both members of a pair fold onto the canonical member,
+  and orientation is ignored.
+- ``reldir``: the canonical relation and the mention's direction relative to it. A
+  passive mention of ``R`` is ``(R, inverse)`` whether or not the KB holds a converse
+  entry for ``R``.
+
+**Why the selection objective uses ``reldir``.** The weak-label matcher prefers the label
+whose own reading has the mention's voice. A passive mention of a relation with a
+converse entry is therefore stored under that entry, which is its own ``raw`` class. A
+passive mention of a relation without one is stored under the active label with
+``direction=inverse``, sharing the active class. Scored against ``raw`` labels, an
+agreement objective rewards separating the voices of some relations and penalizes it for
+others, depending only on which converse entries the KB happens to hold. ``reldir``
+treats every relation alike. ``rel`` folds the voices together, so it would reward
+clusters that merge them and remove the only direction signal an embedding-only linker
+has.
+
+**What views never use.** Views are built from the KB's declarations only. Judgements
+*about* the KB — that two entries mean the same thing in text, or that one entry covers
+two senses — form a scoring-only reference inventory. They are deliberately not accepted
+here, so they cannot leak into the training labels of the fit they are used to score.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import pathlib
+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import Literal, get_args
+
+import pandas as pd
+
+ClassView = Literal["raw", "rel", "reldir"]
+CLASS_VIEWS: tuple[ClassView, ...] = get_args(ClassView)
+
+CANONICAL_COLUMNS = ("is_canonical", "canonical_entity_id")
+
+DIRECTION_FORWARD = "forward"
+DIRECTION_INVERSE = "inverse"
+DIRECTION_SYMMETRIC = "symmetric"
+
+RELATION_COLUMN = "relation"
+"""Canonical relation label of a mention row (added by :func:`add_view_columns`)."""
+RELATION_DIRECTION_COLUMN = "relation_direction"
+"""Direction of a mention row relative to its canonical relation."""
+ENTITY_CLASS_COLUMN = "entity_class"
+"""The row's class key in the chosen view; agreement metrics score against it."""
+VIEW_COLUMNS = (RELATION_COLUMN, RELATION_DIRECTION_COLUMN, ENTITY_CLASS_COLUMN)
+
+CLASS_SEPARATOR = "|"
+"""Joins relation and direction in a ``reldir`` class key (``"regulates|inverse"``)."""
+
+_DERIVATION_SCRIPT = "run/preprocessing/derive_inverse_pairs.py"
+_OPPOSITE = {DIRECTION_FORWARD: DIRECTION_INVERSE, DIRECTION_INVERSE: DIRECTION_FORWARD}
+
+
+def _suggest_pairs_path(kb_csv_path: str | pathlib.Path | None) -> str:
+    """The sibling ``*.pairs.csv`` to point at, if one is on disk next to the input."""
+    if kb_csv_path is None:
+        return ""
+    path = pathlib.Path(kb_csv_path)
+    stem = path.name.split(".csv")[0]
+    # `properties.synthesis.2.inverse` -> `properties.synthesis.2`: the derivation reads
+    # the inverse file and writes the pairs file beside it.
+    base = stem[: -len(".inverse")] if stem.endswith(".inverse") else stem
+    candidate = path.with_name(f"{base}.pairs.csv")
+    return str(candidate) if candidate.exists() else ""
+
+
+def require_canonical_kb(
+    kb: pd.DataFrame, *, kb_csv_path: str | pathlib.Path | None = None
+) -> None:
+    """Raise unless ``kb`` carries the converse-pair derivation's columns."""
+    missing = [c for c in CANONICAL_COLUMNS if c not in kb.columns]
+    if not missing:
+        return
+    where = f"{kb_csv_path}: " if kb_csv_path else ""
+    suggestion = _suggest_pairs_path(kb_csv_path)
+    hint = (
+        f"Pass {suggestion} instead."
+        if suggestion
+        else f"Build one with {_DERIVATION_SCRIPT} and pass its *.pairs.csv output."
+    )
+    raise ValueError(
+        f"{where}KB is missing {', '.join(missing)}, so it predates the converse-pair "
+        f"derivation. A *.inverse.csv is the *input* to {_DERIVATION_SCRIPT}, not its "
+        f"output. {hint}"
+    )
+
+
+def canonical_id_map(
+    kb: pd.DataFrame, *, kb_csv_path: str | pathlib.Path | None = None
+) -> dict[str, str]:
+    """Every KB id → the canonical id of its converse pair.
+
+    Built from the **whole** KB, not the canonical half, since its purpose is to accept a
+    converse-member id and fold it onto the pair's canonical member. Ids absent from the
+    map are left alone by callers: an id the KB does not know is not this function's to
+    rewrite.
+    """
+    require_canonical_kb(kb, kb_csv_path=kb_csv_path)
+    mapping: dict[str, str] = {}
+    for _, row in kb.iterrows():
+        entity_id = str(row["entity_id"])
+        canonical = row.get("canonical_entity_id")
+        mapping[entity_id] = (
+            str(canonical) if isinstance(canonical, str) and canonical else entity_id
+        )
+    return mapping
+
+
+def symmetric_ids(kb: pd.DataFrame) -> frozenset[str]:
+    """Ids of relations with no orientation ("interacts with", "overlaps").
+
+    Their only valid ``direction`` is ``symmetric``: a reversed mention is the same
+    relation read the same way. A KB without ``is_symmetric`` marks nothing.
+    """
+    if "is_symmetric" not in kb.columns:
+        return frozenset()
+    flagged = kb["is_symmetric"].fillna(False).astype(bool)
+    return frozenset(kb.loc[flagged, "entity_id"].astype(str))
+
+
+def file_sha256(path: str | pathlib.Path) -> str:
+    """Content hash of a KB file, recorded wherever a view's meaning depends on it."""
+    return hashlib.sha256(pathlib.Path(path).read_bytes()).hexdigest()
+
+
+@dataclass(frozen=True)
+class KbClasses:
+    """The label-level facts a view needs, read from a pairs KB.
+
+    Attributes:
+        canonical_label: Every KB label → the label of its pair's canonical member (a
+            label that is its own canonical member maps to itself).
+        converse_labels: Labels whose own reading is the converse of their canonical
+            member ("regulated by" for ``regulates``). A direction measured against one
+            of these is flipped to be measured against the canonical member.
+        symmetric_labels: Labels of relations with no orientation, including every label
+            whose canonical member is symmetric.
+    """
+
+    canonical_label: Mapping[str, str]
+    converse_labels: frozenset[str]
+    symmetric_labels: frozenset[str]
+
+    @classmethod
+    def from_kb(
+        cls, kb: pd.DataFrame, *, kb_csv_path: str | pathlib.Path | None = None
+    ) -> KbClasses:
+        """Read the view facts from a pairs KB; any other KB is refused."""
+        require_canonical_kb(kb, kb_csv_path=kb_csv_path)
+        labels = kb["label"].astype(str).str.strip()
+        duplicated = sorted(set(labels[labels.duplicated()]))
+        if duplicated:
+            raise ValueError(
+                f"KB labels must be unique to define classes; duplicated: {duplicated}"
+            )
+        label_of = dict(zip(kb["entity_id"].astype(str), labels))
+        canon_ids = canonical_id_map(kb, kb_csv_path=kb_csv_path)
+        symmetric = symmetric_ids(kb)
+        canonical_label: dict[str, str] = {}
+        converse: set[str] = set()
+        symmetric_labels: set[str] = set()
+        for entity_id, label in label_of.items():
+            canonical_id = canon_ids.get(entity_id, entity_id)
+            canonical_label[label] = label_of.get(canonical_id, label)
+            if canonical_id != entity_id:
+                converse.add(label)
+            if entity_id in symmetric or canonical_id in symmetric:
+                symmetric_labels.add(label)
+        return cls(
+            canonical_label=canonical_label,
+            converse_labels=frozenset(converse - symmetric_labels),
+            symmetric_labels=frozenset(symmetric_labels),
+        )
+
+    @classmethod
+    def from_csv(cls, path: str | pathlib.Path) -> KbClasses:
+        """:meth:`from_kb` on a KB CSV file."""
+        return cls.from_kb(pd.read_csv(path), kb_csv_path=path)
+
+
+def relation_direction(label: str, direction: object, classes: KbClasses) -> str:
+    """A mention's direction relative to the canonical member of its label's pair.
+
+    ``direction`` is relative to ``label`` itself, as the weak-label matcher records it.
+    A missing value (a lexical, non-verb match) reads the label's own way, so it counts
+    as ``forward``. A symmetric relation is always ``symmetric``.
+    """
+    if label in classes.symmetric_labels or direction == DIRECTION_SYMMETRIC:
+        return DIRECTION_SYMMETRIC
+    own = direction if direction in _OPPOSITE else DIRECTION_FORWARD
+    assert isinstance(own, str)
+    return _OPPOSITE[own] if label in classes.converse_labels else own
+
+
+def class_key(relation: str, direction: str, view: ClassView, *, label: str) -> str:
+    """The class a mention belongs to in ``view``."""
+    if view == "raw":
+        return label
+    if view == "rel" or direction == DIRECTION_SYMMETRIC:
+        return relation
+    return f"{relation}{CLASS_SEPARATOR}{direction}"
+
+
+def add_view_columns(
+    frame: pd.DataFrame,
+    classes: KbClasses,
+    view: ClassView,
+    *,
+    passthrough_labels: frozenset[str] = frozenset(),
+    entity_column: str = "entity",
+    direction_column: str = "direction",
+) -> pd.DataFrame:
+    """A copy of ``frame`` with :data:`VIEW_COLUMNS` added; ``entity`` is left as is.
+
+    Args:
+        frame: Mention rows with a label column and, optionally, the matcher's
+            direction column.
+        classes: View facts from :meth:`KbClasses.from_kb`.
+        view: Which class key :data:`ENTITY_CLASS_COLUMN` carries.
+        passthrough_labels: Labels outside the KB that keep themselves as relation and
+            class (the synthetic negative label).
+
+    Raises:
+        ValueError: A label is neither in the KB nor passed through. A view over a KB
+            the mentions were not labelled with would silently mean something else.
+    """
+    if view not in CLASS_VIEWS:
+        raise ValueError(f"class view must be one of {CLASS_VIEWS}, got {view!r}")
+    labels = frame[entity_column].astype(str)
+    unknown = sorted(
+        set(labels) - set(classes.canonical_label) - set(passthrough_labels)
+    )
+    if unknown:
+        shown = ", ".join(repr(u) for u in unknown[:10])
+        more = f" (+{len(unknown) - 10} more)" if len(unknown) > 10 else ""
+        raise ValueError(
+            f"{len(unknown)} mention labels are not in the class KB: {shown}{more}. "
+            "Pass the KB the mentions were embedded with."
+        )
+    directions = (
+        frame[direction_column]
+        if direction_column in frame.columns
+        else pd.Series([None] * len(frame), index=frame.index)
+    )
+    relations: list[str] = []
+    rel_dirs: list[str] = []
+    keys: list[str] = []
+    for label, direction in zip(labels, directions):
+        if label in passthrough_labels and label not in classes.canonical_label:
+            relations.append(label)
+            rel_dirs.append(DIRECTION_FORWARD)
+            keys.append(label)
+            continue
+        relation = classes.canonical_label[label]
+        rel_dir = relation_direction(
+            label, None if pd.isna(direction) else direction, classes
+        )
+        relations.append(relation)
+        rel_dirs.append(rel_dir)
+        keys.append(class_key(relation, rel_dir, view, label=label))
+    out = frame.copy()
+    out[RELATION_COLUMN] = relations
+    out[RELATION_DIRECTION_COLUMN] = rel_dirs
+    out[ENTITY_CLASS_COLUMN] = keys
+    return out
+
+
+def check_class_view(view: str, class_kb_path: str | pathlib.Path | None) -> ClassView:
+    """Validate a CLI's ``--class-view`` / ``--class-kb-path`` pair; return the view.
+
+    Raises:
+        ValueError: An unknown view, or a view other than ``raw`` without a KB path.
+    """
+    for known in CLASS_VIEWS:
+        if view == known:
+            if known != "raw" and class_kb_path is None:
+                raise ValueError(
+                    f"--class-view {known} needs --class-kb-path (the pairs KB the "
+                    "mentions were embedded with); pass --class-view raw to score "
+                    "against the matched labels."
+                )
+            return known
+    raise ValueError(f"class view must be one of {CLASS_VIEWS}, got {view!r}")
+
+
+def reference_label_column(frame: pd.DataFrame) -> str:
+    """The column agreement metrics compare clusters against.
+
+    :data:`ENTITY_CLASS_COLUMN` when a view was applied, else the raw ``entity`` label.
+    """
+    return ENTITY_CLASS_COLUMN if ENTITY_CLASS_COLUMN in frame.columns else "entity"
diff --git a/pelinker/ground_truth.py b/pelinker/kb/ground_truth.py
similarity index 68%
rename from pelinker/ground_truth.py
rename to pelinker/kb/ground_truth.py
index 15c8fbd..824eae5 100644
--- a/pelinker/ground_truth.py
+++ b/pelinker/kb/ground_truth.py
@@ -20,6 +20,11 @@
 :attr:`GroundTruthScore.n_id_comparable` before trusting
 :attr:`GroundTruthScore.entity_accuracy` — when it is 0, the ids were never comparable
 and the metric is undefined rather than bad.
+
+There is a second id space to reconcile: the KB holds both members of many converse pairs,
+while gold is annotated in *canonical* space with orientation carried in ``direction``.
+Pass ``canonicalize`` so a converse-member id counts as the relation it names rather than
+as a wrong answer.
 """
 
 from __future__ import annotations
@@ -31,19 +36,55 @@
 
 MatchMode = "overlap", "exact"
 
+GT_DIRECTIONS = ("forward", "inverse", "symmetric", "na")
+"""Predicate orientation relative to the KB entry's canonical argument order.
+
+``forward`` — surface arguments follow the KB entry (``X activates Y``);
+``inverse`` — reversed, typically passive voice or a converse phrasing
+(``Y is activated by X``); ``symmetric`` — the predicate has no orientation
+(``X interacts with Y``); ``na`` — orientation not annotated / not applicable.
+"""
+
 
 @dataclass(frozen=True)
 class GtSpan:
-    """One ground-truth annotation: a character range in a document, plus its KB id."""
+    """One ground-truth annotation: a character range in a document, plus its KB id.
+
+    The required core (``itext``/``a``/``b``/``entity_id``) is what the scorer consumes.
+    The optional fields carry annotation provenance and the directionality contract;
+    older ``*.gt.json`` files without them load unchanged.
+    """
 
     itext: int
     a: int
     b: int
     entity_id: str | None = None
 
+    direction: str | None = None
+    """One of :data:`GT_DIRECTIONS`, or ``None`` for legacy annotations."""
+    subject_span: tuple[int, int] | None = None
+    object_span: tuple[int, int] | None = None
+    surface: str | None = None
+    annotator: str | None = None
+    source: str | None = None
+    """Annotation channel, e.g. ``"llm"`` or ``"human"``."""
+    confidence: float | None = None
+
     def __post_init__(self) -> None:
         if self.b <= self.a:
             raise ValueError(f"span end must exceed start, got a={self.a} b={self.b}")
+        if self.direction is not None and self.direction not in GT_DIRECTIONS:
+            raise ValueError(
+                f"direction must be one of {GT_DIRECTIONS}, got {self.direction!r}"
+            )
+
+
+def _optional_span(value: object, *, field: str) -> tuple[int, int] | None:
+    if value is None:
+        return None
+    if not isinstance(value, (list, tuple)) or len(value) != 2:
+        raise ValueError(f"{field} must be a [start, end] pair, got {value!r}")
+    return int(value[0]), int(value[1])
 
 
 def load_ground_truth_spans(path: pathlib.Path | str) -> list[GtSpan]:
@@ -65,6 +106,27 @@ def load_ground_truth_spans(path: pathlib.Path | str) -> list[GtSpan]:
                     entity_id=(
                         None if hit.get("entity_id") is None else str(hit["entity_id"])
                     ),
+                    direction=(
+                        None if hit.get("direction") is None else str(hit["direction"])
+                    ),
+                    subject_span=_optional_span(
+                        hit.get("subject_span"), field="subject_span"
+                    ),
+                    object_span=_optional_span(
+                        hit.get("object_span"), field="object_span"
+                    ),
+                    surface=(
+                        None if hit.get("surface") is None else str(hit["surface"])
+                    ),
+                    annotator=(
+                        None if hit.get("annotator") is None else str(hit["annotator"])
+                    ),
+                    source=(None if hit.get("source") is None else str(hit["source"])),
+                    confidence=(
+                        None
+                        if hit.get("confidence") is None
+                        else float(hit["confidence"])
+                    ),
                 )
             )
     return spans
@@ -137,6 +199,7 @@ def score_predictions_against_ground_truth(
     *,
     match_mode: str = "overlap",
     predicted_id_to_kb_in: Mapping[str, str] | None = None,
+    canonicalize: Mapping[str, str] | None = None,
 ) -> GroundTruthScore:
     """Greedy one-to-one span matching within each document.
 
@@ -150,6 +213,12 @@ def score_predictions_against_ground_truth(
         predicted_id_to_kb_in: Maps ``entity_id_predicted`` onto input-KB ids so the
             comparison is meaningful. Without it, minted KB-out ids never match and
             ``n_id_comparable`` stays 0.
+        canonicalize: Folds each converse pair onto its canonical member
+            (:func:`pelinker.eval.kb.canonical_id_map`), applied **after**
+            ``predicted_id_to_kb_in`` and to both sides. Gold is annotated in canonical
+            space, so without it a system that answers "regulated by" where gold says
+            ("regulates", inverse) is scored wrong for picking the other name of the
+            relation it got right. Ids the map does not mention pass through untouched.
 
     Matching is greedy in document order, and each gold span is consumed at most once, so
     duplicate predictions over one annotation count as false positives rather than
@@ -203,8 +272,12 @@ def score_predictions_against_ground_truth(
                 # Unmappable prediction: not comparable, so it neither helps nor hurts.
                 continue
             pred_id = str(mapped)
+        gold_id = hit.entity_id
+        if canonicalize is not None:
+            pred_id = str(canonicalize.get(pred_id, pred_id))
+            gold_id = str(canonicalize.get(gold_id, gold_id))
         n_id_comparable += 1
-        if pred_id == hit.entity_id:
+        if pred_id == gold_id:
             n_id_correct += 1
 
     return GroundTruthScore(
diff --git a/pelinker/kb_out.py b/pelinker/kb/kb_out.py
similarity index 72%
rename from pelinker/kb_out.py
rename to pelinker/kb/kb_out.py
index 2d9de9d..518336a 100644
--- a/pelinker/kb_out.py
+++ b/pelinker/kb/kb_out.py
@@ -8,7 +8,7 @@
 
 import pandas as pd
 
-from pelinker.cluster_composition_viz import (
+from pelinker.clustering.composition import (
     HDBSCAN_NOISE_CLUSTER_ID,
     NOISE_CLUSTER_LABEL,
     aggregate_cluster_entity_mass,
@@ -16,13 +16,15 @@
     cluster_score_percentile_summary,
     filter_emergent_assignments,
 )
-from pelinker.config import ClusterCompositionSnapshot, KBConfig
-from pelinker.linker_cluster_training import (
+from pelinker.core.config import ClusterCompositionSnapshot, KBConfig
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.kb.classes import RELATION_DIRECTION_COLUMN
+from pelinker.linker.cluster_training import (
     _disambiguate_consensus_names,
     consensus_cluster_names,
 )
 
-KB_OUT_SCHEMA = "pelinker.kb_out.v1"
+KB_OUT_SCHEMA = "pelinker.kb.kb_out.v1"
 _MAX_SPLIT_MENTIONS = 500
 _SLUG_INVALID_RE = re.compile(r"[^a-zA-Z0-9._-]+")
 
@@ -44,6 +46,10 @@ class KbOutFitProvenance:
     min_cluster_size: int
     clustering_sample_index: int = 0
     seed: int | None = None
+    class_view: str | None = None
+    """Class view the fit's agreement metrics scored against (:mod:`pelinker.kb.classes`)."""
+    class_kb_sha256: str | None = None
+    """Content hash of the pairs KB that view was computed from."""
 
 
 def kb_slug_from_config(kb_config: KBConfig | None, *, fallback: str = "kb") -> str:
@@ -231,7 +237,7 @@ def build_kb_out_catalog(
     naming: KbOutNamingConfig | None = None,
     kb_in_labels_map_path: str | None = None,
 ) -> dict[str, Any]:
-    """Build the canonical KB-out catalog (schema ``pelinker.kb_out.v1``)."""
+    """Build the canonical KB-out catalog (schema ``pelinker.kb.kb_out.v1``)."""
     naming_cfg = naming or KbOutNamingConfig()
     kb_slug = kb_slug_from_config(kb_config)
 
@@ -363,6 +369,10 @@ def build_kb_out_catalog(
     }
     if fit_provenance.seed is not None:
         fit_prov["seed"] = int(fit_provenance.seed)
+    if fit_provenance.class_view is not None:
+        fit_prov["class_view"] = fit_provenance.class_view
+    if fit_provenance.class_kb_sha256 is not None:
+        fit_prov["class_kb_sha256"] = fit_provenance.class_kb_sha256
 
     return {
         "schema": KB_OUT_SCHEMA,
@@ -392,6 +402,112 @@ def build_kb_out_catalog(
     }
 
 
+def kb_out_to_kb_in_map(catalog: dict[str, Any]) -> dict[str, str]:
+    """Minted KB-out entity id → dominant input-KB entity id.
+
+    Each catalog cluster carries its ``components`` (KB-in entity *labels* with
+    within-cluster mass, dominant first) and the KB-in provenance block carries the
+    ``entity_id → label`` map. Resolving the dominant component's label back to its id
+    yields the translation :func:`~pelinker.kb.ground_truth
+    .score_predictions_against_ground_truth` needs as ``predicted_id_to_kb_in`` — without
+    it, minted ids (``kb::C0007``) never match gold ids (``PEL.000032``) and entity
+    accuracy stays undefined.
+
+    Clusters whose dominant component is the synthetic negative label, or whose label has
+    no KB-in id, are omitted: predictions there stay non-comparable rather than being
+    scored against an arbitrary id. When several KB-in ids share one label, the
+    lexicographically smallest id wins, deterministically.
+    """
+    kb_in = catalog.get("provenance", {}).get("kb_in", {})
+    id_to_label = kb_in.get("labels_map", {})
+    label_to_id: dict[str, str] = {}
+    for eid in sorted(id_to_label):
+        label = str(id_to_label[eid])
+        label_to_id.setdefault(label, str(eid))
+
+    out: dict[str, str] = {}
+    for cluster in catalog.get("clusters", []):
+        components = cluster.get("components") or []
+        if not components:
+            continue
+        dominant_label = str(components[0]["entity"])
+        if dominant_label == NEGATIVE_LABEL:
+            continue
+        kb_in_id = label_to_id.get(dominant_label)
+        if kb_in_id is None:
+            continue
+        out[str(cluster["entity_id"])] = kb_in_id
+    return out
+
+
+def cluster_direction_summary(
+    assignments: pd.DataFrame,
+    *,
+    direction_column: str = RELATION_DIRECTION_COLUMN,
+) -> dict[int, dict[str, Any]]:
+    """Per emergent cluster: the share of mention mass in each direction, and the mode.
+
+    Directions are relative to each mention's canonical relation (see
+    :func:`pelinker.kb.classes.add_view_columns`), so a cluster that holds both voices of
+    one relation shows up as a mixed cluster here rather than as a merge of two entries.
+    The dominant direction breaks ties by name, deterministically. Noise is excluded.
+    """
+    if direction_column not in assignments.columns or "cluster" not in assignments:
+        return {}
+    work = assignments.loc[
+        assignments["cluster"].astype(int) != HDBSCAN_NOISE_CLUSTER_ID,
+        ["cluster", direction_column],
+    ]
+    out: dict[int, dict[str, Any]] = {}
+    for cid, grp in work.groupby("cluster", sort=True):
+        counts = grp[direction_column].astype(str).value_counts()
+        total = int(counts.sum())
+        if total == 0:
+            continue
+        mix = {str(d): float(n) / total for d, n in sorted(counts.items())}
+        dominant = min(mix, key=lambda d: (-mix[d], d))
+        out[int(cid)] = {
+            "direction_mix": mix,
+            "dominant_direction": dominant,
+            "direction_mentions": total,
+        }
+    return out
+
+
+def annotate_cluster_directions(
+    catalog: dict[str, Any], assignments: pd.DataFrame
+) -> dict[str, str]:
+    """Add ``direction_mix`` / ``dominant_direction`` to each catalog cluster, in place.
+
+    Returns:
+        KB-out entity id → dominant direction, for the clusters that have one. This is
+        the direction the linker emits for a mention it links to that entity.
+    """
+    summary = cluster_direction_summary(assignments)
+    by_entity: dict[str, str] = {}
+    for cluster in catalog.get("clusters", []):
+        info = summary.get(int(cluster["cluster_id"]))
+        if info is None:
+            continue
+        cluster.update(info)
+        by_entity[str(cluster["entity_id"])] = str(info["dominant_direction"])
+    return by_entity
+
+
+def kb_out_to_reldir_map(catalog: dict[str, Any]) -> dict[str, tuple[str, str | None]]:
+    """Minted KB-out entity id → (dominant input-KB id, dominant direction).
+
+    The id half is :func:`kb_out_to_kb_in_map`. The direction is ``None`` for a catalog
+    built without a class view, which carries no per-cluster direction.
+    """
+    ids = kb_out_to_kb_in_map(catalog)
+    direction_of = {
+        str(c["entity_id"]): c.get("dominant_direction")
+        for c in catalog.get("clusters", [])
+    }
+    return {eid: (kb_in, direction_of.get(eid)) for eid, kb_in in ids.items()}
+
+
 def cluster_labels_from_catalog(
     catalog: dict[str, Any],
     *,
diff --git a/pelinker/preprocess.py b/pelinker/kb/preprocess.py
similarity index 100%
rename from pelinker/preprocess.py
rename to pelinker/kb/preprocess.py
diff --git a/pelinker/kb/registry.py b/pelinker/kb/registry.py
new file mode 100644
index 0000000..baf62ac
--- /dev/null
+++ b/pelinker/kb/registry.py
@@ -0,0 +1,28 @@
+"""Locating versioned knowledge-base files on disk."""
+
+import re
+
+_SYNTHESIS_RE = re.compile(r"^properties\.synthesis\.(\d+)\.csv$")
+"""Only canonical ``properties.synthesis.<version>.csv`` files are versions.
+
+Derived variants live beside them (``properties.synthesis.2.diverse.csv``), and matching
+on ``".synthesis." in name`` used to pull those in and then crash converting ``diverse``
+to an int — which made every KB regeneration run fail at the first step.
+"""
+
+
+def fetch_latest_kb(
+    path_derived,
+) -> tuple[str | None, int]:
+    filename_versions = []
+    for file in path_derived.iterdir():
+        if not file.is_file():
+            continue
+        match = _SYNTHESIS_RE.match(file.name)
+        if match is not None:
+            filename_versions.append((file.name, int(match.group(1))))
+    filename_versions.sort(key=lambda x: x[1])
+    if filename_versions:
+        return filename_versions[-1]
+    else:
+        return None, -1
diff --git a/run/obsolete/testing/__init__.py b/pelinker/linker/__init__.py
similarity index 100%
rename from run/obsolete/testing/__init__.py
rename to pelinker/linker/__init__.py
diff --git a/pelinker/linker_cluster_training.py b/pelinker/linker/cluster_training.py
similarity index 99%
rename from pelinker/linker_cluster_training.py
rename to pelinker/linker/cluster_training.py
index f9bedaf..0854dfc 100644
--- a/pelinker/linker_cluster_training.py
+++ b/pelinker/linker/cluster_training.py
@@ -6,7 +6,7 @@
 
 import pandas as pd
 
-from pelinker.config import ClusterCompositionSnapshot
+from pelinker.core.config import ClusterCompositionSnapshot
 
 
 def _modal_cluster_deterministic(clusters: list[int]) -> int | None:
diff --git a/pelinker/distillation.py b/pelinker/linker/distillation.py
similarity index 98%
rename from pelinker/distillation.py
rename to pelinker/linker/distillation.py
index 527b653..eb4b22f 100644
--- a/pelinker/distillation.py
+++ b/pelinker/linker/distillation.py
@@ -20,7 +20,7 @@
    well the student imitates the teacher's *extrapolation errors*. Pooling the two hides
    which is degrading.
 2. **Forced noise.** The head is trained only on ``cluster != -1``
-   (:func:`~pelinker.entity_head._non_noise_xy`), so it *structurally cannot* emit noise:
+   (:func:`~pelinker.linker.entity_head._non_noise_xy`), so it *structurally cannot* emit noise:
    every mention the teacher would have abstained on gets assigned some cluster. The
    abstain responsibility silently moves to the screeners and ``thr_score``, so we report
    what fraction of teacher-noise rows get forced and with what confidence.
@@ -43,9 +43,9 @@
 import pandas as pd
 from sklearn.metrics import adjusted_rand_score
 
-from pelinker.config import DistillationGateConfig
-from pelinker.entity_head import EntityHead
-from pelinker.linker_cluster_training import cluster_composition_from_training_frame
+from pelinker.core.config import DistillationGateConfig
+from pelinker.linker.entity_head import EntityHead
+from pelinker.linker.cluster_training import cluster_composition_from_training_frame
 
 logger = logging.getLogger(__name__)
 
@@ -153,7 +153,7 @@ def cluster_to_entity_map(
 ) -> dict[int, str]:
     """Majority KB entity per emergent cluster (noise excluded).
 
-    Reuses :func:`~pelinker.linker_cluster_training.cluster_composition_from_training_frame`
+    Reuses :func:`~pelinker.linker.cluster_training.cluster_composition_from_training_frame`
     so the student/teacher comparison resolves clusters exactly the way the fitted linker
     does, rather than via a parallel majority-vote implementation.
     """
diff --git a/pelinker/entity_head.py b/pelinker/linker/entity_head.py
similarity index 100%
rename from pelinker/entity_head.py
rename to pelinker/linker/entity_head.py
diff --git a/pelinker/linker_kb_lemma.py b/pelinker/linker/kb_lemma.py
similarity index 96%
rename from pelinker/linker_kb_lemma.py
rename to pelinker/linker/kb_lemma.py
index 631bd1a..6c4e412 100644
--- a/pelinker/linker_kb_lemma.py
+++ b/pelinker/linker/kb_lemma.py
@@ -5,8 +5,8 @@
 from dataclasses import dataclass
 from typing import Any, Sequence
 
-from pelinker.onto import WordGrouping, _wg_for_property
-from pelinker.util import text_to_tokens
+from pelinker.core.onto import WordGrouping, _wg_for_property
+from pelinker.text.tokenize import text_to_tokens
 
 
 def build_kb_lemma_index(
@@ -15,7 +15,7 @@ def build_kb_lemma_index(
     """Build ``{word_grouping: {lemma_string: kb_training_entity_label}}`` from ``labels_map`` values.
 
     Mirrors the per-property lemma matching used at training time in
-    :func:`pelinker.util.extract_and_embed_mentions` (``_wg_for_property`` for the bucket,
+    :func:`pelinker.text.embed.extract_and_embed_mentions` (``_wg_for_property`` for the bucket,
     lemma strings for comparison), inverted to an O(1) lookup keyed by mention lemma.
     """
     index: dict[WordGrouping, dict[str, str]] = {}
diff --git a/pelinker/matching.py b/pelinker/matching.py
deleted file mode 100644
index 61366ec..0000000
--- a/pelinker/matching.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import re
-
-
-def match_pattern(pattern, text, suffix_length=1, buffer_length=10):
-    """
-
-        given the pattern, split it into words, match ordered groups of words truncating each of at size n-1
-            if its size > 5. Allow for pieces of text, spaces and dashes in between of size at most 10.
-    :param pattern:
-    :param text:
-    :param suffix_length: remove word ending `induces` -> `induce`
-    :param buffer_length: allow for words (and dashes) in between
-    :return:
-    """
-    pattern_words = pattern.split()
-    pattern_words_limit = [
-        rf"\b{re.escape(word[:-suffix_length] if len(word) > 5 and suffix_length is not None and suffix_length > 0 else word)}\w*"
-        for word in pattern_words
-    ]
-    buffer = rf"\s+[\w\s-]{{0,{buffer_length - 1}}}"
-    regex_pattern = buffer.join(pattern_words_limit) + r"\b"
-    matches = [(m.start(), m.end()) for m in re.finditer(regex_pattern, text)]
-    return matches
-
-
-def match_pieces(pattern, text, suffix_length=1, matches=None):
-    """
-        given the pattern and the match of constituent words in the sense of match_pattern,
-        for each match find the boundaries of matching words
-    :param pattern:
-    :param text:
-    :param suffix_length:
-    :param matches:
-    :return:
-    """
-    if matches is None:
-        matches = []
-    pattern_words = pattern.split()
-
-    r = {k: [] for k in matches}
-    if matches:
-        pats = [
-            rf"\b{re.escape(word[:-suffix_length] if len(word) > 5 else word)}\w*\b"
-            for word in pattern_words
-        ]
-        for pat in pats:
-            word_matches = [(m.start(), m.end()) for m in re.finditer(pat, text)]
-
-            for x, y in matches:
-                r[(x, y)] += [
-                    (a, b) for a, b in word_matches if min([y, b]) - max([x, a]) > 0
-                ]
-    return r
diff --git a/pelinker/model.py b/pelinker/model.py
index cbdd50e..eb35464 100644
--- a/pelinker/model.py
+++ b/pelinker/model.py
@@ -17,7 +17,7 @@
 import pathlib
 import logging
 
-from pelinker.config import (
+from pelinker.core.config import (
     ClusterCompositionSnapshot,
     ClusteringOptimizationConfig,
     EmbeddingModelMetadata,
@@ -28,37 +28,43 @@
     NegativeScreenerConfig,
     TransformConfig,
 )
-from pelinker.distillation import (
+from pelinker.linker.distillation import (
     DistillationFidelityMetrics,
     apply_gates,
     cluster_to_entity_map,
     evaluate_distillation_fidelity,
     grouped_holdout_split,
 )
-from pelinker.entity_head import EntityHead, fit_mlp_entity_head
-from pelinker.screener.projection_screener import (
+from pelinker.linker.entity_head import EntityHead, fit_mlp_entity_head
+from pelinker.screener.projection import (
     ManifoldOovScoreModel,
     build_projection_training_arrays,
     evaluate_projection_cv,
     fit_projection_lda_no_cv,
     fit_projection_score_model,
 )
-from pelinker.screener.ambient_screener import NegativeClassScreener
-from pelinker.analysis import (
+from pelinker.screener.ambient import NegativeClassScreener
+from pelinker.screener.evaluation import (
     fit_ambient_screener_with_metrics,
     split_by_negative_label,
 )
-from pelinker.clustering_fit import fit_manifold_clustering
-from pelinker.embedder import embed_kb_corpus
-from pelinker.embedding_fusion import (
+from pelinker.clustering.fit import fit_manifold_clustering
+from pelinker.embed.corpus import embed_kb_corpus
+from pelinker.data.fusion import (
     MENTION_PROVENANCE_COLUMNS,
     fused_property_vectors_from_paths,
     property_fused_dataframe_for_linker_order,
 )
-from pelinker.sampling import draw_selection_sample, stratified_mention_sample
-from pelinker.scaling import MinClusterSizeProvenance, resolve_min_cluster_size
-from pelinker.selection import load_selection_frame
-from pelinker.transform import (
+from pelinker.search.sampling import draw_selection_sample, stratified_mention_sample
+from pelinker.core.scaling import MinClusterSizeProvenance, resolve_min_cluster_size
+from pelinker.kb.classes import (
+    RELATION_COLUMN,
+    RELATION_DIRECTION_COLUMN,
+    VIEW_COLUMNS,
+    file_sha256,
+)
+from pelinker.search.selection import load_selection_frame
+from pelinker.clustering.transform import (
     EmbeddingTransformer,
     TransformArtifacts,
     load_clustering_manifold,
@@ -67,42 +73,40 @@
     score_transform_artifacts,
     is_parametric_umap,
 )
-from pelinker.reporting import (
+from pelinker.data.frames import entity_negative_label_mask_01
+from pelinker.reports.schema import (
     ClusteringFitMetrics,
     ClusteringHyperparameters,
     LinkerFitDiagnostics,
     ModelSelectionReport,
     NegativeScreenerInSampleMetrics,
-    entity_negative_label_mask_01,
     subsample_diagnostics_stratified,
 )
-from pelinker.kb_out import (
+from pelinker.kb.kb_out import (
     KbOutFitProvenance,
     KbOutNamingConfig,
+    annotate_cluster_directions,
     build_kb_out_catalog,
 )
-from pelinker.linker_cluster_training import (
+from pelinker.linker.cluster_training import (
     cluster_composition_from_training_frame,
     consensus_cluster_names,
     provisional_cluster_assignments_from_training_frame as _provisional_cluster_assignments_from_training_frame,
 )
-from pelinker.linker_kb_lemma import (
+from pelinker.linker.kb_lemma import (
     build_kb_lemma_index,
     enrich_entity_predictions_kb_validation,
     lookup_kb_training_entity_label,
 )
-from pelinker.onto import (
+from pelinker.core.onto import (
     MAX_LENGTH,
     MentionCandidate,
     NEGATIVE_LABEL,
     WordGrouping,
 )
-from pelinker.util import (
-    extract_ordered_mention_tensors,
-    keep_expression_for_prediction,
-    load_models,
-    texts_to_vrep,
-)
+from pelinker.text.embed import extract_ordered_mention_tensors, texts_to_vrep
+from pelinker.text.models import load_models
+from pelinker.text.tokenize import keep_expression_for_prediction
 
 logger = logging.getLogger(__name__)
 
@@ -130,7 +134,7 @@
     "kb_in_entity_clusters": {},
     "cluster_id_to_entity_id": {},
     "kb_out_catalog": None,
-    "nlp_model_name": "en_core_web_trf",
+    "nlp_model_name": "en_core_web_lg",
     "_nlp": None,
     "screener_in_sample_metrics": None,
     "clustering_fit_metrics": None,
@@ -139,6 +143,7 @@
     "predict_mode": "legacy",
     "min_cluster_size_provenance": None,
     "distillation_fidelity": None,
+    "cluster_direction": {},
 }
 
 
@@ -621,6 +626,11 @@ def _build_training_cluster_frame(
     for col in MENTION_PROVENANCE_COLUMNS:
         if col in manifold_full.columns:
             frame[col] = manifold_full[col].values
+    # The matcher's direction and the class-view columns travel with each mention, so
+    # the catalog can read merges between relations and a direction per cluster.
+    for col in ("direction", *VIEW_COLUMNS):
+        if col in manifold_full.columns:
+            frame[col] = manifold_full[col].values
     frame["cluster"] = cluster_labels
     frame["screener_score"] = screener_decision[manifold_mask]
     frame["projection_score"] = projection_scores[manifold_mask]
@@ -660,23 +670,31 @@ def _finalize_linker_cluster_state(
     kb_out_naming: KbOutNamingConfig | None = None,
     kb_in_labels_map_path: str | None = None,
 ) -> None:
+    tcf = linker.training_cluster_frame
+    assert tcf is not None
+    # Under a class view, composition is read between canonical relations: the two
+    # voices of one relation are one entity whose clusters differ in direction, not two
+    # entities a cluster merged. Direction is summarized per cluster below.
+    composition_frame = (
+        tcf.assign(entity=tcf[RELATION_COLUMN])
+        if RELATION_COLUMN in tcf.columns
+        else tcf
+    )
     linker.cluster_composition = cluster_composition_from_training_frame(
-        linker.training_cluster_frame
+        composition_frame
     )
     linker.cluster_consensus_names = consensus_cluster_names(linker.cluster_composition)
 
     linker.kb_in_labels_map = dict(linker.labels_map)
     linker.kb_in_entity_clusters = _provisional_cluster_assignments_from_training_frame(
         linker.kb_in_labels_map,
-        linker.training_cluster_frame,
+        composition_frame,
     )
     linker.cluster_assignments = dict(linker.kb_in_entity_clusters)
 
-    tcf = linker.training_cluster_frame
-    assert tcf is not None
     assign_cols = ["entity", "cluster", "pmid", "mention"]
     assign_cols.extend(c for c in MENTION_PROVENANCE_COLUMNS if c in tcf.columns)
-    assignments = tcf[assign_cols].copy()
+    assignments = composition_frame[assign_cols].copy()
 
     linker.kb_out_catalog = build_kb_out_catalog(
         linker.cluster_composition,
@@ -687,6 +705,11 @@ def _finalize_linker_cluster_state(
         naming=kb_out_naming,
         kb_in_labels_map_path=kb_in_labels_map_path,
     )
+    linker.cluster_direction = (
+        annotate_cluster_directions(linker.kb_out_catalog, tcf)
+        if RELATION_DIRECTION_COLUMN in tcf.columns
+        else {}
+    )
     labels_map = linker.kb_out_catalog["labels_map"]
     linker.labels_map = {str(k): str(v) for k, v in labels_map.items()}
     linker.cluster_derived_labels_map = dict(linker.labels_map)
@@ -848,6 +871,9 @@ def __init__(
         self.kb_in_labels_map: dict[str, str] = {}
         self.kb_in_entity_clusters: dict[str, int] = {}
         self.cluster_id_to_entity_id: dict[int, str] = {}
+        self.cluster_direction: dict[str, str] = {}
+        """KB-out entity id → dominant direction of its training mentions, relative to
+        the canonical relation. Empty for a fit without a class view."""
         self.kb_out_catalog: dict[str, object] | None = None
         self.screener: NegativeClassScreener | None = None
         self.screener_in_sample_metrics: NegativeScreenerInSampleMetrics | None = None
@@ -865,7 +891,7 @@ def __init__(
         self._hf_tokenizer = None
         self._hf_model = None
         self._hf_models_by_type: dict[str, tuple[object, object]] = {}
-        self.nlp_model_name: str = kwargs.pop("nlp_model_name", "en_core_web_trf")
+        self.nlp_model_name: str = kwargs.pop("nlp_model_name", "en_core_web_lg")
         self._nlp: object | None = None
         self._fit_clustering_report: ModelSelectionReport | None = None
 
@@ -955,7 +981,7 @@ def load(cls, file_spec: str | pathlib.Path) -> Linker:
 
     def take_fit_clustering_report(self) -> ModelSelectionReport | None:
         """
-        Consume the :class:`~pelinker.reporting.ClusteringReport` produced by the last :meth:`fit`.
+        Consume the :class:`~pelinker.reports.schema.ModelSelectionReport` produced by the last :meth:`fit`.
 
         Call **before** :meth:`dump` if you need JSON or other persistence: the report is
         not serialized on the linker artifact (only prediction state is pickled).
@@ -984,7 +1010,7 @@ def build_clustering_report(
         training_diagnostics: LinkerFitDiagnostics | None = None,
     ) -> ModelSelectionReport | None:
         """
-        Build a :class:`~pelinker.reporting.ClusteringReport` when full training rows exist.
+        Build a :class:`~pelinker.reports.schema.ModelSelectionReport` when full training rows exist.
 
         After a normal :meth:`fit`, heavy training payloads are removed for prediction; use
         :meth:`take_fit_clustering_report` immediately after fitting instead.
@@ -1125,15 +1151,15 @@ def fit(
         Args:
             embeddings: Path or sequence of paths to parquet file(s) (mention-level rows:
                         ``pmid``, ``entity``, ``mention``, ``embed``). Multiple files are
-                        fused like :func:`~pelinker.selection.load_selection_frame` (inner join
+                        fused like :func:`~pelinker.search.selection.load_selection_frame` (inner join
                         on keys, concat embeddings). Order must match
                         ``embedding_metadata.sources``. If None, ``embed_kb_corpus`` is run
                         (one output file per source).
             transform_config: TransformConfig instance
             min_cluster_size: HDBSCAN ``min_cluster_size`` (choose upstream, e.g. via
-                ``pelinker.model_selection``). When ``None``, it is resolved from
+                ``pelinker.search.model_selection``). When ``None``, it is resolved from
                 ``fit_config.scale_curve`` against the realized manifold row count, or
-                falls back to :data:`~pelinker.scaling.DEFAULT_MIN_CLUSTER_SIZE`. An
+                falls back to :data:`~pelinker.core.scaling.DEFAULT_MIN_CLUSTER_SIZE`. An
                 explicit value always wins; either way the choice and its origin land on
                 ``min_cluster_size_provenance`` and in the fit report.
             fit_config: Parquet read batching, mention load filters, subsample settings, and screener config.
@@ -1148,10 +1174,10 @@ def fit(
             Sets ``cluster_composition`` (mention-weighted property mass and per-cluster
             mixtures), ``cluster_consensus_names`` (short labels from those mixtures),
             ``screener_in_sample_metrics``, and ``clustering_fit_metrics``. Mention-level
-            training tables and manifold arrays used for :class:`~pelinker.reporting.ClusteringReport`
+            training tables and manifold arrays used for :class:`~pelinker.reports.schema.ModelSelectionReport`
             are stripped after each fit; persist JSON with :meth:`take_fit_clustering_report` and
-            :func:`~pelinker.reporting.write_clustering_report_json` at
-            :func:`~pelinker.reporting.linker_fit_clustering_report_path` (same layout as
+            :func:`~pelinker.reports.io.write_clustering_report_json` at
+            :func:`~pelinker.reports.paths.linker_fit_clustering_report_path` (same layout as
             ``pelinker-fit`` ``report_path``) before :meth:`dump`.
 
         Returns:
@@ -1421,6 +1447,12 @@ def _fit_clustering_on_prepared_mentions(
                 min_cluster_size=min_cluster_size,
                 clustering_sample_index=fit_cfg.clustering_sample_index,
                 seed=fit_cfg.base_seed,
+                class_view=fit_cfg.class_view,
+                class_kb_sha256=(
+                    file_sha256(fit_cfg.class_kb_path)
+                    if fit_cfg.class_kb_path is not None
+                    else None
+                ),
             ),
             kb_out_naming=kb_out_naming,
             kb_in_labels_map_path=kb_in_labels_map_path,
@@ -1494,7 +1526,8 @@ def _ensure_nlp(self) -> object:
             import spacy
 
             logger.info("Loading spaCy model %r for predict()", self.nlp_model_name)
-            self._nlp = spacy.load(self.nlp_model_name)
+            # Entities are never read; NER is a large share of spaCy time.
+            self._nlp = spacy.load(self.nlp_model_name, exclude=["ner"])
         return self._nlp
 
     @staticmethod
@@ -1703,7 +1736,7 @@ def _encode_mentions(
         ``ichunk``, ``word_grouping`` and ``lemma`` (space-joined token lemmas, used for
         KB-match lookups).
 
-        Mentions are filtered with :func:`~pelinker.util.keep_expression_for_prediction`
+        Mentions are filtered with :func:`~pelinker.text.tokenize.keep_expression_for_prediction`
         (drop windows containing punctuation; drop windows whose tokens are all stop
         words).
         """
@@ -1817,7 +1850,7 @@ def predict(
 
         Tokenization uses the spaCy pipeline named by ``nlp_model_name`` (set from
         ``EmbeddingTrainingConfig.nlp_model`` during corpus embedding, else default
-        ``en_core_web_trf``).
+        ``en_core_web_lg``).
 
         Each ``entities`` row includes ``score``: HDBSCAN approximate cluster
         membership probability from ``approximate_predict`` on UMAP coordinates.
@@ -2081,6 +2114,10 @@ def _predict_with_clustering(
                 projection_score=float(oov_scores_k[j]),
             )
             cast(dict[str, object], row)["mention_source_index"] = int(mention_i)
+            if self.cluster_direction:
+                cast(dict[str, object], row)["direction_predicted"] = (
+                    self.cluster_direction.get(predicted_entity)
+                )
             candidates.append(row)
 
         deduped = self._dedupe_overlapping_prediction_rows(candidates)
@@ -2111,7 +2148,7 @@ def _predict_with_clustering(
         return deduped, None
 
     def _kb_lemma_index_by_wg(self, nlp: object) -> dict[WordGrouping, dict[str, str]]:
-        """Build lemma→KB training-entity index; see :func:`pelinker.linker_kb_lemma.build_kb_lemma_index`."""
+        """Build lemma→KB training-entity index; see :func:`pelinker.linker.kb_lemma.build_kb_lemma_index`."""
         return build_kb_lemma_index(self.labels_map, nlp)
 
     def compute_mention_anomaly(
diff --git a/pelinker/onto.py b/pelinker/onto.py
deleted file mode 100644
index 064f2fc..0000000
--- a/pelinker/onto.py
+++ /dev/null
@@ -1,251 +0,0 @@
-import dataclasses
-from dataclasses import field
-from enum import Enum
-from dataclass_wizard import JSONWizard
-import torch
-from collections import defaultdict
-from collections.abc import Iterator
-
-MAX_LENGTH = 512
-
-
-class WordGrouping(Enum):
-    W1 = 1
-    W2 = 2
-    W3 = 3
-    W4 = 4
-
-
-class BaseDataclass(JSONWizard, JSONWizard.Meta):
-    key_transform_with_dump = "SNAKE"
-    skip_defaults = True
-
-
-NEGATIVE_LABEL = "__NEGATIVE__"
-
-
-@dataclasses.dataclass
-class ChunkMapper(BaseDataclass):
-    """Maps encoder chunks back to documents and optional pooled span rows.
-
-    When :func:`pelinker.util.texts_to_vrep` runs multiple ``word_modes``, it calls
-    :func:`pelinker.util.render_elementary_tensor_table` repeatedly on the **same**
-    instance. Fields ``text_word_spans_list``, ``token_word_spans_list``,
-    ``tt_expressions``, and ``mapping_table`` therefore reflect **only the last**
-    grouping pass; read per-mode results from :class:`ReportBatch` instead.
-    """
-
-    tensor: torch.Tensor  # n_layers x n_batch x n_len x n_emb - tensor where n_batch dim goes over all chunks
-    chunks: list[str]  # flat list of chunks
-    token_spans_list: list[
-        list[tuple[int, int]]
-    ]  # for each chunk contains a list of token spans
-    it_ic: list[tuple[int, int]]
-    cumulative_lens: list[list[int]]
-    text_word_spans_list: list[list[tuple[int, int]]] | None = None
-    token_word_spans_list: list[list[tuple[int, int]]] | None = None
-    mapping_table: list[tuple[int, int, tuple[int, int], tuple[int, int]]] | None = None
-    text_chunk_map: defaultdict[int, list] = field(
-        default_factory=lambda: defaultdict(list)
-    )
-    tt_expressions: list[torch.Tensor] = field(
-        default_factory=list
-    )  # n_expressions [n_len x n_emb]
-
-    def set_token_word_spans(self, word_int_bounds):
-        from pelinker.util import map_words_to_tokens_list
-
-        self.token_word_spans_list, self.text_word_spans_list = (
-            map_words_to_tokens_list(self.token_spans_list, word_int_bounds)
-        )
-
-    def set_mapping_table(self):
-        it_ic = sorted(self.it_ic)
-        self.mapping_table = []
-        self.text_chunk_map = defaultdict(list)
-
-        if self.text_word_spans_list is None:
-            pass
-        for (ichunk, (itext, ichunk_local)), chsp in zip(
-            enumerate(it_ic), self.text_word_spans_list
-        ):
-            self.text_chunk_map[itext].append(ichunk)
-            chunk_offset = self.cumulative_lens[itext][ichunk_local]
-            for a, b in chsp:
-                self.mapping_table += [
-                    (itext, ichunk, (a, b), (a + chunk_offset, b + chunk_offset))
-                ]
-
-    def map_chunk_to_text(self, itext, ichunk_local, a=0):
-        return a + self.cumulative_lens[itext][ichunk_local]
-
-    def ichunk_to_itext_ichunk_local(self, ichunk: int):
-        return self.it_ic[ichunk]
-
-    def ichunk_char_offset(self, ichunk: int):
-        itext, ichunk_local = self.ichunk_to_itext_ichunk_local(ichunk)
-        return self.cumulative_lens[itext][ichunk_local]
-
-
-@dataclasses.dataclass
-class SimplifiedToken(BaseDataclass):
-    ix: int
-    ix_end: int
-    text: str
-    lemma: str
-    tag: str
-    pos: str | None = None  # spaCy ``token.pos_``; ``None`` only for legacy payloads
-    is_stop: bool | None = None  # spaCy ``token.is_stop``
-
-
-@dataclasses.dataclass
-class Expression(BaseDataclass):
-    tokens: list[SimplifiedToken]
-    itext: int | None = None  # index of document
-    ichunk: int | None = None  # index of document chunk
-    a: int | None = None  # index of the first character
-    b: int | None = None  # exclusive end index (Python slice semantics)
-
-    def __post_init__(self):
-        self.tokens = sorted(self.tokens, key=lambda x: x.ix)
-        self.a = self.tokens[0].ix
-        self.b = self.tokens[-1].ix_end
-
-
-@dataclasses.dataclass
-class MentionCandidate(BaseDataclass):
-    """Typed mention payload used by :class:`pelinker.model.Linker` predictions."""
-
-    mention: str
-    a: int | None
-    b: int | None
-    a_abs: int | None = None
-    b_abs: int | None = None
-    itext: int | None = None
-    ichunk: int | None = None
-    word_grouping: WordGrouping | None = None
-    lemma: str = ""
-
-
-@dataclasses.dataclass
-class ExpressionHolder(BaseDataclass):
-    tt: torch.Tensor
-    expressions: list[Expression]
-
-    def __post_init__(self):
-        if len(self.expressions) != self.tt.shape[0]:
-            raise ValueError(
-                "The number of expressions does not match the shape of the tensor"
-            )
-
-    def filter_on_lemmas(
-        self, tokens: list[SimplifiedToken]
-    ) -> list[tuple[Expression, torch.Tensor]]:
-        return list(self.iter_on_lemmas(tokens))
-
-    def iter_on_lemmas(
-        self, tokens: list[SimplifiedToken]
-    ) -> Iterator[tuple[Expression, torch.Tensor]]:
-        tokens_lemmatized = " ".join(e.lemma for e in tokens)
-        for expression, embedding in zip(self.expressions, self.tt):
-            if (
-                " ".join(token.lemma for token in expression.tokens)
-                == tokens_lemmatized
-            ):
-                yield expression, embedding
-
-
-@dataclasses.dataclass
-class ExpressionHolderBatch(BaseDataclass):
-    expression_data: list[ExpressionHolder]
-    word_grouping: WordGrouping | None = None
-
-
-@dataclasses.dataclass
-class ReportBatch(BaseDataclass):
-    """Batch of texts with shared encoder state and one holder list per ``WordGrouping``.
-
-    ``chunk_mapper`` is shared across groupings; its span/pooling fields may match
-    only the **last** mode processed—use ``_data`` / ``__getitem__`` for mode-specific
-    embeddings and expressions.
-    """
-
-    _data: list[ExpressionHolderBatch]
-    texts: list[str]
-    chunk_mapper: ChunkMapper
-
-    def __post_init__(self):
-        for item in self._data:
-            if len(self.texts) != len(item.expression_data):
-                raise ValueError(
-                    "The number of ExpressionHolders does not match the number of texts"
-                )
-
-    def __getitem__(self, wg: WordGrouping) -> ExpressionHolderBatch:
-        filtered = [item for item in self._data if item.word_grouping == wg]
-        if filtered:
-            return filtered[0]
-        else:
-            raise ValueError(f"{wg} not available")
-
-    def get_data_for_grouping(self, wg: WordGrouping):
-        return [item for item in self._data if item.word_grouping == wg]
-
-    def available_groupings(self):
-        return [item.word_grouping for item in self._data]
-
-    def get_text_embeddings(self, layers_spec: str | list[int]):
-        """
-        Extract sentence-level embeddings for each text in the batch.
-
-        Args:
-            layers_spec: Layer specification (string digits or negative indices); see
-                :func:`pelinker.util.normalize_layers_spec`. Not ``\"sent\"``.
-
-        Returns:
-            list[torch.Tensor]: List of embeddings, one per text in self.texts
-        """
-        from pelinker.util import normalize_layers_spec, tt_aggregate_normalize
-
-        layers = normalize_layers_spec(
-            layers_spec,
-            n_hidden_states=self.chunk_mapper.tensor.shape[0],
-        )
-        # Extract chunk-level embeddings from chunk_mapper.tensor
-        # chunk_mapper.tensor has shape: n_layers x n_chunks x n_tokens x n_emb
-        chunk_embeddings = tt_aggregate_normalize(self.chunk_mapper.tensor, layers)
-
-        # Map chunks back to texts
-        # Group chunks by text index
-        text_chunks = {}
-        for ichunk, (itext, ichunk_local) in enumerate(self.chunk_mapper.it_ic):
-            if itext not in text_chunks:
-                text_chunks[itext] = []
-            text_chunks[itext].append(ichunk)
-
-        # Aggregate embeddings for each text (average if multiple chunks)
-        text_embeddings = []
-        for itext in range(len(self.texts)):
-            chunk_indices = text_chunks.get(itext, [])
-            if chunk_indices:
-                # Get embeddings for all chunks of this text
-                chunk_embs = chunk_embeddings[chunk_indices]
-                # Average over chunks if multiple chunks
-                if len(chunk_indices) > 1:
-                    text_emb = chunk_embs.mean(dim=0)
-                else:
-                    text_emb = chunk_embs[0]
-                text_embeddings.append(text_emb)
-            else:
-                # Fallback: create zero embedding if no chunks found
-                # This shouldn't happen, but handle it gracefully
-                text_embeddings.append(torch.zeros_like(chunk_embeddings[0]))
-
-        return text_embeddings
-
-
-def _wg_for_property(prop: str) -> WordGrouping | None:
-    n = len(prop.split())
-    if n in (1, 2, 3, 4):
-        return WordGrouping(n)
-    return None
diff --git a/pelinker/plotting.py b/pelinker/plotting.py
index c65988f..83a607e 100644
--- a/pelinker/plotting.py
+++ b/pelinker/plotting.py
@@ -1,4 +1,5 @@
 import colorsys
+import logging
 import pathlib
 import re
 
@@ -7,21 +8,21 @@
 import pandas as pd
 
 from pySankey.sankey import sankey
-from pelinker.analysis import (
+from pelinker.search.grid_solver import (
     grid_solver_overrides_active,
     resolve_chosen_min_cluster_size_by_combo_from_grid,
     should_resolve_chosen_min_cluster_size,
     solve_pooled_grid_from_metrics_list,
 )
-from pelinker.clustering_grid import SmoothedGridOptimumResult
-from pelinker.config import ClusteringOptimizationConfig, GridObjectiveSpec
-from pelinker.grid_export import (
+from pelinker.clustering.grid import SmoothedGridOptimumResult
+from pelinker.core.config import ClusteringOptimizationConfig, GridObjectiveSpec
+from pelinker.search.grid_export import (
     apply_chosen_min_cluster_size_to_grid,
     has_grid_points_for_dbcv_ari_scatter,
     select_grid_points_at_chosen_min_cluster_size,
 )
-from pelinker.reporting import LinkerFitDiagnostics, ModelSelectionReport
-from pelinker.scaling import ScaleCurve
+from pelinker.reports.schema import LinkerFitDiagnostics, ModelSelectionReport
+from pelinker.core.scaling import ScaleCurve
 import seaborn as sns
 
 # Force a non-interactive backend because this project only saves plots to files.
@@ -32,6 +33,8 @@
 from matplotlib.patches import Ellipse, Patch, Polygon, Rectangle, RegularPolygon, Wedge
 from plotly import express as px, graph_objects as go
 
+logger = logging.getLogger(__name__)
+
 # χ²(2) critical value at p≈0.95 for Gaussian 95% contour (no scipy).
 _CHI2_PPF_95_DF2 = 5.991464550106692
 
@@ -354,7 +357,7 @@ def plot_dbcv_vs_ari_from_grid(
     grid_cluster_count_reward: float | None = None,
     grid_n_entities: int | None = None,
     grid_objective: GridObjectiveSpec | None = None,
-    optimization_method: str | None = None,
+    grid_one_se_k: float | None = None,
 ) -> bool:
     """
     Scatter of mean DBCV vs mean ARI per (model, layer); shape = arity (△/□/○),
@@ -381,7 +384,7 @@ def plot_dbcv_vs_ari_from_grid(
         grid_cluster_count_reward=grid_cluster_count_reward,
         grid_n_entities=grid_n_entities,
         grid_objective=grid_objective,
-        optimization_method=optimization_method,
+        grid_one_se_k=grid_one_se_k,
     ):
         chosen_by_combo = resolve_chosen_min_cluster_size_by_combo_from_grid(
             df_grid,
@@ -389,7 +392,7 @@ def plot_dbcv_vs_ari_from_grid(
             grid_cluster_count_reward=grid_cluster_count_reward,
             grid_n_entities=grid_n_entities,
             grid_objective=grid_objective,
-            optimization_method=optimization_method,
+            grid_one_se_k=grid_one_se_k,
         )
         if chosen_by_combo:
             df_plot = apply_chosen_min_cluster_size_to_grid(df_grid, chosen_by_combo)
@@ -524,6 +527,8 @@ def plot_dbcv_vs_ari_from_grid(
             frameon=True,
         )
 
+    _draw_pareto_front(ax, df)
+
     ax.grid(True, alpha=0.28, linestyle="--", zorder=0)
     plt.tight_layout()
     _save_figure_multi_format(fig, output_path)
@@ -531,6 +536,174 @@ def plot_dbcv_vs_ari_from_grid(
     return True
 
 
+def pareto_front_mask(dbcv: np.ndarray, ari: np.ndarray) -> np.ndarray:
+    """Boolean mask of points not dominated on ``(dbcv, ari)``, both maximized.
+
+    A point is dominated when another is at least as good on both metrics and strictly
+    better on one. The front is the honest answer to "maximize both": every scalar score,
+    including the geometric mean, just picks one point off it.
+    """
+    d = np.asarray(dbcv, dtype=np.float64)
+    a = np.asarray(ari, dtype=np.float64)
+    n = d.size
+    mask = np.zeros(n, dtype=bool)
+    for i in range(n):
+        if not (np.isfinite(d[i]) and np.isfinite(a[i])):
+            continue
+        dominated = (
+            (d >= d[i])
+            & (a >= a[i])
+            & ((d > d[i]) | (a > a[i]))
+            & np.isfinite(d)
+            & np.isfinite(a)
+        )
+        mask[i] = not bool(np.any(dominated))
+    return mask
+
+
+def _draw_pareto_front(ax: plt.Axes, df: pd.DataFrame) -> None:
+    """Outline the non-dominated (model, layer) candidates on the DBCV-vs-ARI scatter."""
+    centroids = (
+        df.groupby(["model", "layer"], sort=False)[["dbcv", "ari"]].mean().reset_index()
+    )
+    if len(centroids) < 2:
+        return
+    d = centroids["dbcv"].to_numpy(dtype=np.float64)
+    a = centroids["ari"].to_numpy(dtype=np.float64)
+    front = pareto_front_mask(d, a)
+    if not np.any(front):
+        return
+    order = np.argsort(d[front])
+    ax.plot(
+        d[front][order],
+        a[front][order],
+        linestyle="--",
+        linewidth=1.4,
+        color="0.35",
+        alpha=0.8,
+        marker="none",
+        zorder=1,
+        label="Pareto front",
+    )
+
+
+def _draw_grid_objective_panel(
+    ax: plt.Axes,
+    grid_solve: SmoothedGridOptimumResult | None,
+    *,
+    color: str,
+) -> str:
+    """Draw the combined-objective curve with its one-SE decision band. Returns the title.
+
+    Shows *why* a ``min_cluster_size`` was chosen: the pooled objective with its paired
+    standard errors, the ``mu[argmax] - k*se`` threshold, which grid points are
+    statistically tied with the best, and where the rule landed.
+    """
+    if grid_solve is None or len(grid_solve.x) == 0:
+        return "Grid objective (unavailable)"
+
+    x = np.array(grid_solve.x, dtype=np.float64)
+    y_obj = np.array(grid_solve.y_objective, dtype=np.float64)
+    y_smooth = np.array(grid_solve.y_smooth, dtype=np.float64)
+    se = np.array(grid_solve.y_se, dtype=np.float64)
+
+    if se.size == y_smooth.size and np.any(se > 0):
+        ax.fill_between(
+            x,
+            y_smooth - se,
+            y_smooth + se,
+            color=color,
+            alpha=0.18,
+            linewidth=0,
+            label="±1 paired SE",
+            zorder=0,
+        )
+
+    ax.plot(
+        x,
+        y_obj,
+        marker="s",
+        color=color,
+        linewidth=1.2,
+        markersize=5,
+        alpha=0.55,
+        label="objective (raw)",
+        zorder=2,
+    )
+    if np.any(np.isfinite(y_smooth)):
+        ax.plot(
+            x,
+            y_smooth,
+            color=color,
+            linewidth=2.2,
+            label="objective (smoothed)",
+            zorder=3,
+        )
+
+    eligible = np.array(grid_solve.y_eligible, dtype=bool)
+    if eligible.size == x.size and np.any(eligible):
+        ax.plot(
+            x[eligible],
+            y_smooth[eligible],
+            linestyle="none",
+            marker="o",
+            markersize=11,
+            markerfacecolor="none",
+            markeredgecolor=color,
+            markeredgewidth=1.4,
+            label="within 1 SE of best",
+            zorder=4,
+        )
+
+    argmax_x = grid_solve.argmax_min_cluster_size
+    if argmax_x is not None:
+        i_star = int(np.argmin(np.abs(x - float(argmax_x))))
+        if np.isfinite(y_smooth[i_star]):
+            ax.axhline(
+                y_smooth[i_star],
+                color="0.45",
+                linestyle=":",
+                linewidth=1.2,
+                label="best mean",
+                zorder=1,
+            )
+            if grid_solve.one_se_k > 0 and se.size == x.size:
+                # Each point is tested against its own paired SE, so the decision boundary
+                # is a curve, not a level.
+                ax.plot(
+                    x,
+                    y_smooth[i_star] - grid_solve.one_se_k * se,
+                    color="0.45",
+                    linestyle="--",
+                    linewidth=1.0,
+                    label=f"best − {grid_solve.one_se_k:g}·SE",
+                    zorder=1,
+                )
+        ax.plot(
+            [float(argmax_x)],
+            [y_smooth[i_star]],
+            marker="*",
+            markersize=14,
+            color="#333333",
+            linestyle="none",
+            label="argmax",
+            zorder=5,
+        )
+
+    has_cluster_term = any(
+        abs(v) > 1e-12 for v in grid_solve.y_cluster_term if np.isfinite(v)
+    )
+    bits = ["pooled"]
+    if has_cluster_term:
+        bits.append("cluster reward")
+    if grid_solve.n_samples:
+        bits.append(f"n={grid_solve.n_samples}")
+    suffix = ", ".join(bits)
+    if grid_solve.selection == "degenerate_fallback":
+        return f"Grid objective — DEGENERATE, fell back to DBCV ({suffix})"
+    return f"Grid objective ({suffix}); pick: {grid_solve.selection}"
+
+
 def plot_metrics_with_error_bars(
     metrics_list: list[pd.DataFrame],
     output_path: pathlib.Path,
@@ -541,7 +714,7 @@ def plot_metrics_with_error_bars(
     grid_cluster_count_reward: float | None = None,
     grid_n_entities: int | None = None,
     grid_objective: GridObjectiveSpec | None = None,
-    optimization_method: str | None = None,
+    grid_one_se_k: float | None = None,
 ):
     """
     Plot metrics across multiple runs with error bars using seaborn lineplot.
@@ -553,29 +726,39 @@ def plot_metrics_with_error_bars(
         grid_solve: Precomputed pooled grid diagnostics (avoids a second solve; drives objective panel).
         optimization_config: When set (or when any grid override kwarg is set and ``chosen_min_cluster_size``
             is omitted), re-run the pooled grid solver for the vertical marker.
-        grid_cluster_count_reward: Override :attr:`~pelinker.config.ClusteringOptimizationConfig.grid_cluster_count_reward`.
-        grid_n_entities: Override :attr:`~pelinker.config.ClusteringOptimizationConfig.grid_n_entities`.
-        grid_objective: Override :attr:`~pelinker.config.ClusteringOptimizationConfig.grid_objective`.
-        optimization_method: Override :attr:`~pelinker.config.ClusteringOptimizationConfig.optimization_method`.
+        grid_cluster_count_reward: Override :attr:`~pelinker.core.config.ClusteringOptimizationConfig.grid_cluster_count_reward`.
+        grid_n_entities: Override :attr:`~pelinker.core.config.ClusteringOptimizationConfig.grid_n_entities`.
+        grid_objective: Override :attr:`~pelinker.core.config.ClusteringOptimizationConfig.grid_objective`.
+        grid_one_se_k: Override :attr:`~pelinker.core.config.ClusteringOptimizationConfig.grid_one_se_k`.
     """
-    if grid_solve is None and should_resolve_chosen_min_cluster_size(
+    # A supplied chosen_min_cluster_size must not suppress the objective *curve* — only
+    # override the value. Solving for diagnostics whenever they are missing is what keeps
+    # the objective panel from silently rendering blank on the live-run path.
+    override_wins = should_resolve_chosen_min_cluster_size(
         chosen_min_cluster_size=chosen_min_cluster_size,
         optimization_config=optimization_config,
         grid_cluster_count_reward=grid_cluster_count_reward,
         grid_n_entities=grid_n_entities,
         grid_objective=grid_objective,
-        optimization_method=optimization_method,
-    ):
-        grid_solve = solve_pooled_grid_from_metrics_list(
-            metrics_list,
-            optimization_config,
-            grid_cluster_count_reward=grid_cluster_count_reward,
-            grid_n_entities=grid_n_entities,
-            grid_objective=grid_objective,
-            optimization_method=optimization_method,
-        )
+        grid_one_se_k=grid_one_se_k,
+    )
+    if grid_solve is None and metrics_list:
+        try:
+            grid_solve = solve_pooled_grid_from_metrics_list(
+                metrics_list,
+                optimization_config,
+                grid_cluster_count_reward=grid_cluster_count_reward,
+                grid_n_entities=grid_n_entities,
+                grid_objective=grid_objective,
+                grid_one_se_k=grid_one_se_k,
+            )
+        except ValueError as exc:
+            # Diagnostics are best-effort; never fail a plot over them.
+            logger.debug(
+                "Grid objective diagnostics unavailable for %s: %s", output_path, exc
+            )
 
-    if grid_solve is not None and chosen_min_cluster_size is None:
+    if grid_solve is not None and (chosen_min_cluster_size is None or override_wins):
         chosen_min_cluster_size = float(grid_solve.chosen_min_cluster_size)
 
     # Combine all metrics DataFrames, adding a run_id column
@@ -681,45 +864,13 @@ def _maybe_vline(ax: plt.Axes) -> None:
     )
 
     obj_color = colors[3]
-    if grid_solve is not None and len(grid_solve.x) > 0:
-        x_obj = np.array(grid_solve.x, dtype=np.float64)
-        y_obj = np.array(grid_solve.y_objective, dtype=np.float64)
-        y_smooth = np.array(grid_solve.y_smooth, dtype=np.float64)
-        ax_obj.plot(
-            x_obj,
-            y_obj,
-            marker="s",
-            color=obj_color,
-            linewidth=2,
-            markersize=7,
-            label="objective",
-            zorder=2,
-        )
-        if np.any(np.isfinite(y_smooth)):
-            ax_obj.plot(
-                x_obj,
-                y_smooth,
-                linestyle="--",
-                color=obj_color,
-                linewidth=1.5,
-                alpha=0.65,
-                label="smoothed",
-                zorder=1,
-            )
-        has_cluster_term = any(
-            abs(v) > 1e-12 for v in grid_solve.y_cluster_term if np.isfinite(v)
-        )
-        obj_title = (
-            "Grid objective (pooled + cluster penalty)"
-            if has_cluster_term
-            else "Grid objective (pooled)"
-        )
-    else:
-        obj_title = "Grid objective (unavailable)"
+    obj_title = _draw_grid_objective_panel(ax_obj, grid_solve, color=obj_color)
     _maybe_vline(ax_obj)
     _style_metrics_axis(
         ax_obj, ylabel="Grid objective", title=obj_title, color=obj_color
     )
+    if grid_solve is not None and len(grid_solve.x) > 0:
+        ax_obj.legend(fontsize=8, loc="best", framealpha=0.85)
 
     plt.tight_layout()
     _save_figure_multi_format(fig, output_path)
@@ -1315,7 +1466,20 @@ def plot_cluster_viz(
     fig.write_html(str(output_path), include_plotlyjs=True, full_html=True)
 
 
-def plot_metrics(df: pd.DataFrame, output_path: pathlib.Path) -> None:
+def plot_metrics(
+    df: pd.DataFrame,
+    output_path: pathlib.Path,
+    *,
+    chosen_min_cluster_size: float | None = None,
+    grid_solve: SmoothedGridOptimumResult | None = None,
+    optimization_config: ClusteringOptimizationConfig | None = None,
+) -> None:
+    """Single-sample version of :func:`plot_metrics_with_error_bars`.
+
+    With one sample the paired standard errors are all zero, so the objective panel shows
+    the argmax of the smoothed curve with no decision band — which is exactly what the
+    solver does in that case.
+    """
     if df.empty:
         return
 
@@ -1327,15 +1491,37 @@ def plot_metrics(df: pd.DataFrame, output_path: pathlib.Path) -> None:
         )
         return
 
+    if grid_solve is None:
+        try:
+            grid_solve = solve_pooled_grid_from_metrics_list([df], optimization_config)
+        except ValueError as exc:
+            logger.debug(
+                "Grid objective diagnostics unavailable for %s: %s", output_path, exc
+            )
+    if grid_solve is not None and chosen_min_cluster_size is None:
+        chosen_min_cluster_size = float(grid_solve.chosen_min_cluster_size)
+
     has_ari = "ari" in df_plot.columns and bool(df_plot["ari"].notna().any())
-    colors = ["#2E86AB", "#A23B72", "#C44E52"]  # Blue, Purple, Red
+    colors = ["#2E86AB", "#A23B72", "#C44E52", "#F18F01"]  # Blue, Purple, Red, Orange
 
     if has_ari:
-        fig, axes = plt.subplots(1, 3, figsize=(18, 5))
-        ax_dbcv, ax_ari, ax_k = axes[0], axes[1], axes[2]
+        fig, axes = plt.subplots(1, 4, figsize=(24, 5))
+        ax_dbcv, ax_ari, ax_k, ax_obj = axes[0], axes[1], axes[2], axes[3]
     else:
-        fig, axes = plt.subplots(1, 2, figsize=(12, 5))
-        ax_dbcv, ax_k = axes[0], axes[1]
+        fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+        ax_dbcv, ax_k, ax_obj = axes[0], axes[1], axes[2]
+
+    def _maybe_vline(ax: plt.Axes) -> None:
+        if chosen_min_cluster_size is None:
+            return
+        ax.axvline(
+            chosen_min_cluster_size,
+            color="0.35",
+            linestyle="--",
+            linewidth=1.5,
+            alpha=0.9,
+            zorder=0,
+        )
 
     ax_dbcv.plot(
         df_plot["min_cluster_size"],
@@ -1344,6 +1530,7 @@ def plot_metrics(df: pd.DataFrame, output_path: pathlib.Path) -> None:
         color=colors[0],
         linewidth=2,
     )
+    _maybe_vline(ax_dbcv)
     _style_metrics_axis(
         ax_dbcv,
         ylabel="DBCV Score",
@@ -1359,6 +1546,7 @@ def plot_metrics(df: pd.DataFrame, output_path: pathlib.Path) -> None:
             color=colors[1],
             linewidth=2,
         )
+        _maybe_vline(ax_ari)
         _style_metrics_axis(
             ax_ari,
             ylabel="ARI",
@@ -1373,6 +1561,7 @@ def plot_metrics(df: pd.DataFrame, output_path: pathlib.Path) -> None:
         color=colors[2],
         linewidth=2,
     )
+    _maybe_vline(ax_k)
     _style_metrics_axis(
         ax_k,
         ylabel="n clusters",
@@ -1380,6 +1569,14 @@ def plot_metrics(df: pd.DataFrame, output_path: pathlib.Path) -> None:
         color=colors[2],
     )
 
+    obj_title = _draw_grid_objective_panel(ax_obj, grid_solve, color=colors[3])
+    _maybe_vline(ax_obj)
+    _style_metrics_axis(
+        ax_obj, ylabel="Grid objective", title=obj_title, color=colors[3]
+    )
+    if grid_solve is not None and len(grid_solve.x) > 0:
+        ax_obj.legend(fontsize=8, loc="best", framealpha=0.85)
+
     plt.tight_layout()
     try:
         _save_figure_multi_format(fig, output_path)
@@ -1632,7 +1829,7 @@ def plot_cluster_entity_sankey(
     """
     if flow_df.empty:
         return []
-    from pelinker.cluster_composition_viz import limit_entity_flow_for_plots
+    from pelinker.clustering.composition import limit_entity_flow_for_plots
 
     work = limit_entity_flow_for_plots(
         flow_df,
@@ -1767,7 +1964,7 @@ def load_pmid_texts(
     chunk_size: int = 10_000,
 ) -> dict[str, str]:
     """Stream a PMID/text table and return rows for the requested ``pmids`` only."""
-    from pelinker.ops import load_pmid_texts_from_table
+    from pelinker.data.tables import load_pmid_texts_from_table
 
     return load_pmid_texts_from_table(table_path, pmids, chunk_size=chunk_size)
 
@@ -1828,7 +2025,7 @@ def filter_assignments_for_cluster_viz(
     hdbscan_fit_scope: bool = True,
 ) -> pd.DataFrame:
     """Restrict cluster viz rows to HDBSCAN-fit + screener/OOV-pass mentions when flagged."""
-    from pelinker.cluster_composition_viz import filter_emergent_assignments
+    from pelinker.clustering.composition import filter_emergent_assignments
 
     out = assign.copy()
     if exclude_noise:
@@ -1851,7 +2048,7 @@ def build_fit_cluster_viz_plot_df(
     hdbscan_fit_scope: bool = True,
     cluster_labels: dict[int, str] | None = None,
 ) -> tuple[pd.DataFrame | None, str]:
-    """Build a :func:`plot_cluster_viz` frame from a :class:`~pelinker.reporting.ModelSelectionReport`."""
+    """Build a :func:`plot_cluster_viz` frame from a :class:`~pelinker.reports.schema.ModelSelectionReport`."""
     cluster_viz = report.cluster_viz
     if cluster_viz is None or cluster_viz.size == 0 or cluster_viz.shape[1] < 1:
         return None, report.cluster_viz_method
diff --git a/pelinker/reporting.py b/pelinker/reporting.py
deleted file mode 100644
index f18890e..0000000
--- a/pelinker/reporting.py
+++ /dev/null
@@ -1,1126 +0,0 @@
-from __future__ import annotations
-
-import gzip
-import json
-import logging
-import math
-import pathlib
-from collections import Counter
-from collections.abc import Callable, Sequence
-from dataclasses import dataclass
-from typing import Any, cast
-
-import numpy as np
-import pandas as pd
-
-from pelinker.config import ScreenerKind
-from pelinker.distillation import DistillationFidelityMetrics
-from pelinker.scaling import MinClusterSizeProvenance
-
-logger = logging.getLogger(__name__)
-
-_JSON_CLUSTERING_REPORT_SCHEMA = "pelinker.clustering_report.v10"
-MODEL_SELECTION_RUN_REPORT_SCHEMA = "pelinker.model_selection.run_report.v2"
-
-
-def entity_negative_label_mask_01(
-    entities: pd.Series | np.ndarray,
-    negative_label: str,
-) -> np.ndarray:
-    """
-    Per-row binary labels aligned with ``entities``: ``1`` if the row's ``entity`` equals
-    ``negative_label`` (same convention as the negative screener positive class), else ``0``.
-    """
-    if isinstance(entities, pd.Series):
-        s = entities.astype(str).to_numpy()
-    else:
-        s = np.asarray(entities).astype(str)
-    if s.size == 0:
-        return np.zeros(0, dtype=np.int64)
-    return (s == negative_label).astype(np.int64, copy=False)
-
-
-def mention_quality_frame(
-    dfr: pd.DataFrame,
-    *,
-    neg_mask: np.ndarray,
-    cluster_kb: np.ndarray,
-    pca_residuals: np.ndarray,
-    pca_mahalanobis: np.ndarray,
-    pca_spectral_entropy: np.ndarray,
-    negative_label: str,
-) -> pd.DataFrame:
-    """Per-mention PCA quality and labels for all rows (KB clustered; negatives cluster=-1)."""
-    optional = ["pmid", "mention"]
-    optional_cols = [c for c in optional if c in dfr.columns]
-    out = dfr[["entity", *optional_cols]].copy()
-    cluster_full = np.full(len(dfr), -1, dtype=np.int64)
-    cluster_full[~neg_mask] = np.asarray(cluster_kb, dtype=np.int64).ravel()
-    out["cluster"] = cluster_full
-    out["oov_label"] = entity_negative_label_mask_01(dfr["entity"], negative_label)
-    out["pca_residual"] = np.asarray(pca_residuals, dtype=np.float64).ravel()
-    out["pca_mahalanobis"] = np.asarray(pca_mahalanobis, dtype=np.float64).ravel()
-    out["pca_spectral_entropy"] = np.asarray(
-        pca_spectral_entropy, dtype=np.float64
-    ).ravel()
-    ordered = [
-        "entity",
-        *optional_cols,
-        "cluster",
-        "oov_label",
-        "pca_residual",
-        "pca_mahalanobis",
-        "pca_spectral_entropy",
-    ]
-    return out[ordered]
-
-
-@dataclass(frozen=True)
-class MeanWithUncertainty:
-    """Sample mean and standard deviation (ddof=1) over repeated runs; ``std=0`` for a single run."""
-
-    mean: float
-    std: float
-
-
-@dataclass(frozen=True)
-class MetricMeanStd:
-    """Mean and spread (sample std over CV folds) for one scalar metric."""
-
-    mean: float
-    std: float
-
-
-@dataclass(frozen=True)
-class BinaryClassifierMetrics:
-    """Precision / recall / F1 / AUC vs negative class (label 1); spread is fold-wise."""
-
-    precision: MetricMeanStd
-    recall: MetricMeanStd
-    f1: MetricMeanStd
-    auc: MetricMeanStd
-
-
-@dataclass(frozen=True)
-class AllScreenerCvResult:
-    """Unified stratified CV: embedding screener (LDA vs SVM), manifold OOV, and stacked score."""
-
-    screener_lda: BinaryClassifierMetrics
-    screener_svm: BinaryClassifierMetrics
-    screener_best_kind: str
-    screener_best: BinaryClassifierMetrics
-    oov_winner_kind: str
-    oov: BinaryClassifierMetrics
-    combined: BinaryClassifierMetrics
-
-
-@dataclass(frozen=True)
-class PerDatapointScores:
-    """Out-of-sample scores per stratified-fold test datapoint."""
-
-    orig_idx: list[int]
-    entity: list[str]
-    y_true: list[int]
-    screener_lda_score: list[float]
-    screener_svm_score: list[float]
-    screener_best_score: list[float]
-    oov_score: list[float]
-    combined_score: list[float]
-
-
-@dataclass(frozen=True)
-class NegativeScreenerInSampleMetrics:
-    """Train-set precision / recall / F1 for detecting ``negative_label`` (binary label 1)."""
-
-    precision: float
-    recall: float
-    f1: float
-    n_kb_mentions: int
-    """Rows whose ``entity`` is not the synthetic negative label (class 0)."""
-    n_negative_label_mentions: int
-    """Rows whose ``entity`` equals the synthetic negative label (class 1)."""
-    kind: ScreenerKind
-
-
-@dataclass(frozen=True)
-class ClusteringFitMetrics:
-    """Fit-time clustering diagnostics at a fixed ``min_cluster_size``."""
-
-    min_cluster_size: int
-    dbcv: float | None
-    """HDBSCAN ``relative_validity_`` when available."""
-    ari: float | None
-    n_clusters_emergent: int
-    noise_fraction: float
-    n_samples: int
-
-
-# --- flat row ↔ BinaryClassifierMetrics (symmetric pair) ---
-
-
-def _binary_metrics_to_jsonable(
-    m: BinaryClassifierMetrics,
-) -> dict[str, dict[str, float]]:
-    return {
-        "precision": {"mean": m.precision.mean, "std": m.precision.std},
-        "recall": {"mean": m.recall.mean, "std": m.recall.std},
-        "f1": {"mean": m.f1.mean, "std": m.f1.std},
-        "auc": {"mean": m.auc.mean, "std": m.auc.std},
-    }
-
-
-def _all_screener_cv_to_jsonable(result: AllScreenerCvResult) -> dict[str, object]:
-    return {
-        "screener_lda": _binary_metrics_to_jsonable(result.screener_lda),
-        "screener_svm": _binary_metrics_to_jsonable(result.screener_svm),
-        "screener_best_kind": result.screener_best_kind,
-        "screener_best": _binary_metrics_to_jsonable(result.screener_best),
-        "oov_winner_kind": result.oov_winner_kind,
-        "oov": _binary_metrics_to_jsonable(result.oov),
-        "combined": _binary_metrics_to_jsonable(result.combined),
-    }
-
-
-def _pool_binary_classifier_metrics_branch(
-    summaries: Sequence[AllScreenerCvResult],
-    branch: Callable[[AllScreenerCvResult], BinaryClassifierMetrics],
-) -> BinaryClassifierMetrics:
-    def _collect(field: str) -> MetricMeanStd:
-        vals = np.array(
-            [getattr(branch(s), field).mean for s in summaries],
-            dtype=np.float64,
-        )
-        return MetricMeanStd(
-            mean=float(np.mean(vals)),
-            std=float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0,
-        )
-
-    return BinaryClassifierMetrics(
-        precision=_collect("precision"),
-        recall=_collect("recall"),
-        f1=_collect("f1"),
-        auc=_collect("auc"),
-    )
-
-
-def _pool_all_screener_cv_results(
-    summaries: Sequence[AllScreenerCvResult],
-) -> AllScreenerCvResult:
-    """Pool mean±std across bootstrap samples from each report's CV ``mean`` fields."""
-    if not summaries:
-        raise ValueError("summaries must be non-empty")
-
-    def _mode_str(values: list[str]) -> str:
-        c = Counter(values)
-        return str(c.most_common(1)[0][0])
-
-    kinds_s = [s.screener_best_kind for s in summaries]
-    kinds_o = [s.oov_winner_kind for s in summaries]
-    return AllScreenerCvResult(
-        screener_lda=_pool_binary_classifier_metrics_branch(
-            summaries, lambda r: r.screener_lda
-        ),
-        screener_svm=_pool_binary_classifier_metrics_branch(
-            summaries, lambda r: r.screener_svm
-        ),
-        screener_best_kind=_mode_str(kinds_s),
-        screener_best=_pool_binary_classifier_metrics_branch(
-            summaries, lambda r: r.screener_best
-        ),
-        oov_winner_kind=_mode_str(kinds_o),
-        oov=_pool_binary_classifier_metrics_branch(summaries, lambda r: r.oov),
-        combined=_pool_binary_classifier_metrics_branch(
-            summaries, lambda r: r.combined
-        ),
-    )
-
-
-@dataclass(frozen=True)
-class ClusteringHyperparameters:
-    """
-    HDBSCAN (and related) choices selected by the grid search / smoother.
-
-    Add fields here as more knobs participate in optimization; call sites then stay typed.
-    """
-
-    min_cluster_size: int
-
-
-@dataclass(frozen=True)
-class HyperparameterSearchStats:
-    """Distribution of chosen hyperparameters across repeated clustering samples."""
-
-    min_cluster_size: MeanWithUncertainty
-
-
-@dataclass(frozen=True)
-class LinkerFitDiagnostics:
-    """Per-row training diagnostics for plotting (often stratified-subsampled)."""
-
-    pca_residual: np.ndarray
-    pca_mahalanobis: np.ndarray
-    pca_spectral_entropy: np.ndarray
-    oov_label: np.ndarray
-    """``1`` iff ``entity == negative_label`` (same convention as :class:`ModelSelectionReport`)."""
-    screener_decision: np.ndarray
-    projection_score: np.ndarray
-    n_total: int
-    """Original mention count before subsampling (same as ``len(prepared)`` at fit time)."""
-    sample_random_state: int
-    """RNG seed used for stratified subsampling (or configured seed when no subsample)."""
-
-
-def _copy_linker_fit_diagnostics(
-    full: LinkerFitDiagnostics, random_state: int
-) -> LinkerFitDiagnostics:
-    return LinkerFitDiagnostics(
-        pca_residual=np.asarray(full.pca_residual, dtype=np.float64).copy(),
-        pca_mahalanobis=np.asarray(full.pca_mahalanobis, dtype=np.float64).copy(),
-        pca_spectral_entropy=np.asarray(
-            full.pca_spectral_entropy, dtype=np.float64
-        ).copy(),
-        oov_label=np.asarray(full.oov_label, dtype=np.int64).copy(),
-        screener_decision=np.asarray(full.screener_decision, dtype=np.float64).copy(),
-        projection_score=np.asarray(full.projection_score, dtype=np.float64).copy(),
-        n_total=full.n_total,
-        sample_random_state=random_state,
-    )
-
-
-def _slice_linker_fit_diagnostics(
-    full: LinkerFitDiagnostics,
-    indices: np.ndarray,
-    random_state: int,
-) -> LinkerFitDiagnostics:
-    return LinkerFitDiagnostics(
-        pca_residual=np.asarray(full.pca_residual, dtype=np.float64)[indices],
-        pca_mahalanobis=np.asarray(full.pca_mahalanobis, dtype=np.float64)[indices],
-        pca_spectral_entropy=np.asarray(full.pca_spectral_entropy, dtype=np.float64)[
-            indices
-        ],
-        oov_label=np.asarray(full.oov_label, dtype=np.int64)[indices],
-        screener_decision=np.asarray(full.screener_decision, dtype=np.float64)[indices],
-        projection_score=np.asarray(full.projection_score, dtype=np.float64)[indices],
-        n_total=full.n_total,
-        sample_random_state=random_state,
-    )
-
-
-def subsample_diagnostics_stratified(
-    full: LinkerFitDiagnostics,
-    *,
-    max_rows: int,
-    random_state: int,
-) -> LinkerFitDiagnostics:
-    """
-    Stratified subsample by ``oov_label`` (preserve class proportions, at least one row per
-    non-empty class when two classes exist). Mirrors logic used in pairgrid plotting.
-    """
-    if max_rows < 1:
-        raise ValueError("max_rows must be >= 1")
-    n_total = full.n_total
-    n = int(len(full.pca_residual))
-    if n != n_total:
-        raise ValueError(
-            f"LinkerFitDiagnostics length mismatch: len(arrays)={n} vs n_total={n_total}"
-        )
-
-    def _copy_same() -> LinkerFitDiagnostics:
-        return _copy_linker_fit_diagnostics(full, random_state)
-
-    if n <= max_rows:
-        return _copy_same()
-
-    rng = np.random.default_rng(random_state)
-    y = np.asarray(full.oov_label, dtype=np.int64).ravel()
-    classes = np.unique(y)
-
-    if len(classes) == 1:
-        idx_all = np.flatnonzero(y == int(classes[0]))
-        k = min(max_rows, len(idx_all))
-        chosen = rng.choice(idx_all, size=k, replace=False)
-    else:
-        parts: list[np.ndarray] = []
-        for cls in classes:
-            idx = np.flatnonzero(y == int(cls))
-            k_i = max(1, int(round(max_rows * len(idx) / n)))
-            k_i = min(k_i, len(idx))
-            parts.append(rng.choice(idx, size=k_i, replace=False))
-        chosen = np.concatenate(parts)
-        if len(chosen) > max_rows:
-            chosen = rng.choice(chosen, size=max_rows, replace=False)
-
-    chosen = np.sort(chosen.astype(np.int64, copy=False))
-    return _slice_linker_fit_diagnostics(full, chosen, random_state)
-
-
-@dataclass
-class ModelSelectionReport:
-    """Report containing clustering analysis results for one sample."""
-
-    hyperparameters: ClusteringHyperparameters
-    best_score: float
-    """DBCV (``relative_validity_``) at the chosen ``min_cluster_size`` (mean when from aggregate)."""
-
-    number_properties: int
-    """Count of distinct KB ``entity`` labels in the frame used for PCA→UMAP (excludes ``pelinker.onto.NEGATIVE_LABEL`` when screening)."""
-
-    n_clusters_emergent: int
-    """HDBSCAN emergent cluster count at :attr:`hyperparameters` ``min_cluster_size``.
-
-    In model-selection this is the grid-optimal ``best_size``, not an arbitrary MCS.
-    To compare with ``Linker.fit`` at a fixed MCS, use
-    :func:`n_clusters_at_min_cluster_size` on :attr:`metrics_df` instead.
-    """
-
-    metrics_df: pd.DataFrame
-    assignments: pd.DataFrame
-    pca_residuals: np.ndarray
-    pca_mahalanobis: np.ndarray
-    pca_spectral_entropy: np.ndarray
-    oov_label: np.ndarray
-    """Per-row OOV mask: ``1`` iff ``entity == negative_label`` (same length as ``pca_residuals``)."""
-    umap_clustering: np.ndarray
-    cluster_viz: np.ndarray
-    cluster_viz_method: str
-    pca_reduced: np.ndarray
-    all_screener_cv: AllScreenerCvResult | None = None
-    """Unified stratified CV for embedding screener, manifold OOV, and stacked score."""
-    screener_oos_datapoints: PerDatapointScores | None = None
-    """Per-datapoint OOS scores (not serialized in JSON clustering report)."""
-    ari: float | None = None
-    training_diagnostics: LinkerFitDiagnostics | None = None
-    """Stratified-subsampled PCA quality + screener / manifold OOV scores (linker fit only)."""
-    mention_quality: pd.DataFrame | None = None
-    """All mentions (pos+neg) with PCA quality scores and oov_label; cluster=-1 for negatives."""
-    distillation_fidelity: DistillationFidelityMetrics | None = None
-    """Held-out agreement between the compact entity head and its HDBSCAN teacher.
-
-    ``None`` for legacy fits (no student) and when the holdout was disabled or skipped.
-    """
-    min_cluster_size_provenance: MinClusterSizeProvenance | None = None
-    """Where :attr:`hyperparameters` ``min_cluster_size`` came from (linker fit only).
-
-    Explicit, extrapolated from a scale curve, or the bare default — recorded so a fitted
-    model never carries an unexplained hyperparameter.
-    """
-    n_rows_realized: int | None = None
-    """Mention rows this draw actually clustered on.
-
-    The realized N, **not** the ``clustering_sample_rows`` cap: when the frame is smaller
-    than the cap, or the cap is unset, the two differ. Sample-size-dependent
-    hyperparameters (``min_cluster_size``, and through it HDBSCAN ``min_samples``) are
-    only interpretable against this number — see :mod:`pelinker.scaling`.
-    """
-
-
-def n_clusters_at_min_cluster_size(
-    metrics_df: pd.DataFrame,
-    min_cluster_size: int,
-) -> int | None:
-    """``n_clusters`` from a grid ``metrics_df`` row at ``min_cluster_size`` (for fit parity)."""
-    if (
-        "min_cluster_size" not in metrics_df.columns
-        or "n_clusters" not in metrics_df.columns
-    ):
-        return None
-    hit = metrics_df.loc[
-        metrics_df["min_cluster_size"] == min_cluster_size, "n_clusters"
-    ]
-    if hit.empty:
-        return None
-    return int(hit.iloc[0])
-
-
-def _json_normalize(obj: object) -> object:
-    """Map values to types accepted by :func:`json.dumps` (no NaN/Inf; no numpy scalars)."""
-    if obj is None or isinstance(obj, (str, bool)):
-        return obj
-    if isinstance(obj, (float, np.floating)):
-        x = float(obj)
-        if math.isnan(x) or math.isinf(x):
-            return None
-        return x
-    if isinstance(obj, (int, np.integer)):
-        return int(obj)
-    if isinstance(obj, dict):
-        return {str(k): _json_normalize(v) for k, v in obj.items()}
-    if isinstance(obj, (list, tuple)):
-        return [_json_normalize(x) for x in obj]
-    logger.warning(
-        "json_normalize: coercing unexpected type %s to str", type(obj).__name__
-    )
-    return str(obj)
-
-
-def _dataframe_to_jsonable_records(df: pd.DataFrame) -> list[dict[str, Any]]:
-    return [
-        {str(k): _json_normalize(v) for k, v in row.items()}
-        for row in df.to_dict(orient="records")
-    ]
-
-
-def _ndarray_to_jsonable_nested(arr: np.ndarray) -> Any:
-    return _json_normalize(np.asarray(arr).tolist())
-
-
-# Basenames for artifacts under one report directory (``pelinker-fit`` / clustering search).
-LINKER_FIT_CLUSTERING_REPORT_BASENAME = "linker_fit.clustering_report.json.gz"
-LINKER_FIT_CLUSTER_COMPOSITION_BASENAME = "linker_fit.cluster_composition.json.gz"
-LINKER_FIT_KB_OUT_BASENAME = "linker_fit.kb_out.json"
-_FIT_CLUSTER_COMPOSITION_SCHEMA = "pelinker.fit_cluster_composition.v2"
-_KB_OUT_SCHEMA = "pelinker.kb_out.v1"
-MODEL_SELECTION_RUN_REPORT_BASENAME = "model_selection.run_report.json.gz"
-MODEL_SELECTION_SUMMARY_JSON_SCHEMA = "pelinker.model_selection.summary.v1"
-MODEL_SELECTION_SUMMARY_JSON_BASENAME = "model_selection.summary.json"
-CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME = "results_grid_per_sample.csv"
-CLUSTERING_SEARCH_GRID_CHOSEN_JSON_BASENAME = "grid_chosen_hyperparameters.json"
-CLUSTERING_SEARCH_FINE_METADATA_BASENAME = "fine_clustering_metadata.jsonl.gz"
-FINE_SCREENER_EVAL_BASENAME = "fine_screener_eval.jsonl.gz"
-MODEL_SELECTION_CHECKPOINT_BASENAME = "model_selection.state.json.gz"
-
-
-def linker_fit_clustering_report_path(report_dir: str | pathlib.Path) -> pathlib.Path:
-    """Filesystem path for the fit-time :class:`ClusteringReport` JSON under ``report_dir``."""
-    return pathlib.Path(report_dir).expanduser() / LINKER_FIT_CLUSTERING_REPORT_BASENAME
-
-
-def linker_fit_cluster_composition_path(
-    report_dir: str | pathlib.Path,
-) -> pathlib.Path:
-    """Filesystem path for the entity-weighted cluster composition artifact."""
-    return (
-        pathlib.Path(report_dir).expanduser() / LINKER_FIT_CLUSTER_COMPOSITION_BASENAME
-    )
-
-
-def linker_fit_kb_out_path(report_dir: str | pathlib.Path) -> pathlib.Path:
-    """Filesystem path for the KB-out catalog JSON under ``report_dir``."""
-    return pathlib.Path(report_dir).expanduser() / LINKER_FIT_KB_OUT_BASENAME
-
-
-def write_cluster_composition_json(
-    path: str | pathlib.Path,
-    composition_df: pd.DataFrame,
-    *,
-    top_n: int = 3,
-    weighting: str = "inv_sqrt_mention_count",
-    exclude_noise: bool = False,
-    summary: dict[str, Any] | None = None,
-    max_clusters_in_rows: int | None = None,
-    indent: int = 2,
-) -> None:
-    """Serialize a processed cluster-composition table written at fit time."""
-    from pelinker.cluster_composition_viz import ENTITY_WEIGHTING_INV_SQRT
-
-    p = pathlib.Path(path).expanduser()
-    p.parent.mkdir(parents=True, exist_ok=True)
-    payload: dict[str, Any] = {
-        "schema": _FIT_CLUSTER_COMPOSITION_SCHEMA,
-        "top_n": int(top_n),
-        "weighting": weighting or ENTITY_WEIGHTING_INV_SQRT,
-        "exclude_noise": bool(exclude_noise),
-        "rows": _dataframe_to_jsonable_records(composition_df),
-    }
-    if summary is not None:
-        payload["summary"] = _json_normalize(summary)
-    if max_clusters_in_rows is not None:
-        payload["max_clusters_in_rows"] = int(max_clusters_in_rows)
-    with gzip.open(p, mode="wt", encoding="utf-8", compresslevel=9) as f:
-        json.dump(payload, f, indent=indent)
-
-
-def read_cluster_composition_json(
-    path: str | pathlib.Path,
-) -> tuple[pd.DataFrame, dict[str, Any]]:
-    """Load a composition table and metadata written by :func:`write_cluster_composition_json`."""
-    p = pathlib.Path(path).expanduser()
-    with gzip.open(p, mode="rt", encoding="utf-8") as f:
-        raw: dict[str, Any] = json.load(f)
-    schema = str(raw.get("schema", ""))
-    if schema not in (
-        "pelinker.fit_cluster_composition.v1",
-        _FIT_CLUSTER_COMPOSITION_SCHEMA,
-    ):
-        raise ValueError(f"Unsupported cluster composition schema: {schema!r}")
-    meta = {k: v for k, v in raw.items() if k != "rows"}
-    return pd.DataFrame(raw["rows"]), meta
-
-
-def write_kb_out_json(
-    path: str | pathlib.Path,
-    payload: dict[str, Any],
-    *,
-    indent: int = 2,
-) -> None:
-    """Write :func:`~pelinker.kb_out.build_kb_out_catalog` output."""
-    p = pathlib.Path(path).expanduser()
-    p.parent.mkdir(parents=True, exist_ok=True)
-    if str(payload.get("schema", "")) != _KB_OUT_SCHEMA:
-        raise ValueError(
-            f"Expected schema {_KB_OUT_SCHEMA!r}, got {payload.get('schema')!r}"
-        )
-    with p.open("w", encoding="utf-8") as f:
-        json.dump(_json_normalize(payload), f, indent=indent, ensure_ascii=False)
-
-
-def read_kb_out_json(path: str | pathlib.Path) -> dict[str, Any]:
-    """Load KB-out catalog JSON."""
-    p = pathlib.Path(path).expanduser()
-    with p.open(encoding="utf-8") as f:
-        raw: dict[str, Any] = json.load(f)
-    if str(raw.get("schema", "")) != _KB_OUT_SCHEMA:
-        raise ValueError(f"Unsupported KB-out schema: {raw.get('schema')!r}")
-    return raw
-
-
-def model_selection_run_report_path(report_dir: str | pathlib.Path) -> pathlib.Path:
-    """Filesystem path for the standardized model-selection aggregate report."""
-    return pathlib.Path(report_dir).expanduser() / MODEL_SELECTION_RUN_REPORT_BASENAME
-
-
-def model_selection_summary_json_path(report_dir: str | pathlib.Path) -> pathlib.Path:
-    """Top-level replot summary (rankings, best combos) as plain JSON."""
-    return pathlib.Path(report_dir).expanduser() / MODEL_SELECTION_SUMMARY_JSON_BASENAME
-
-
-def write_model_selection_summary_json(
-    path: str | pathlib.Path,
-    payload: dict[str, Any],
-    *,
-    indent: int = 2,
-) -> None:
-    p = pathlib.Path(path).expanduser()
-    p.parent.mkdir(parents=True, exist_ok=True)
-    body = cast(dict[str, Any], _json_normalize(payload))
-    with p.open("w", encoding="utf-8") as f:
-        json.dump(body, f, indent=indent, ensure_ascii=False)
-
-
-def _linker_fit_diagnostics_to_jsonable(d: LinkerFitDiagnostics) -> dict[str, Any]:
-    return {
-        "pca_residual": _ndarray_to_jsonable_nested(d.pca_residual),
-        "pca_mahalanobis": _ndarray_to_jsonable_nested(d.pca_mahalanobis),
-        "pca_spectral_entropy": _ndarray_to_jsonable_nested(d.pca_spectral_entropy),
-        "oov_label": _ndarray_to_jsonable_nested(d.oov_label),
-        "screener_decision": _ndarray_to_jsonable_nested(d.screener_decision),
-        "projection_score": _ndarray_to_jsonable_nested(d.projection_score),
-        "n_total": int(d.n_total),
-        "sample_random_state": int(d.sample_random_state),
-    }
-
-
-def _linker_fit_diagnostics_from_jsonable(obj: object) -> LinkerFitDiagnostics | None:
-    if obj is None or not isinstance(obj, dict):
-        return None
-    d = cast(dict[str, Any], obj)
-    needed = (
-        "pca_residual",
-        "pca_mahalanobis",
-        "pca_spectral_entropy",
-        "oov_label",
-        "screener_decision",
-        "projection_score",
-        "n_total",
-        "sample_random_state",
-    )
-    if not all(k in d for k in needed):
-        return None
-    pr = np.asarray(d["pca_residual"], dtype=np.float64)
-    pm = np.asarray(d["pca_mahalanobis"], dtype=np.float64)
-    pe = np.asarray(d["pca_spectral_entropy"], dtype=np.float64)
-    ol = np.asarray(d["oov_label"], dtype=np.int64).ravel()
-    sd = np.asarray(d["screener_decision"], dtype=np.float64).ravel()
-    mo = np.asarray(d["projection_score"], dtype=np.float64).ravel()
-    n_tot = int(d["n_total"])
-    srs = int(d["sample_random_state"])
-    m = len(pr)
-    if not (
-        len(pm) == m and len(pe) == m and len(ol) == m and len(sd) == m and len(mo) == m
-    ):
-        return None
-    return LinkerFitDiagnostics(
-        pca_residual=pr,
-        pca_mahalanobis=pm,
-        pca_spectral_entropy=pe,
-        oov_label=ol,
-        screener_decision=sd,
-        projection_score=mo,
-        n_total=n_tot,
-        sample_random_state=srs,
-    )
-
-
-def clustering_report_to_jsonable_dict(report: ModelSelectionReport) -> dict[str, Any]:
-    """
-    Flatten a :class:`ClusteringReport` into JSON-serializable built-ins (no DataFrames/ndarrays).
-
-    Intended for ``json.dumps`` or for pickling a stable, language-adjacent blob. Schema version
-    is stored under ``"schema"`` for forward compatibility.
-    """
-    ari_out: float | None
-    if report.ari is None:
-        ari_out = None
-    else:
-        ari_f = float(report.ari)
-        ari_out = None if math.isnan(ari_f) or math.isinf(ari_f) else ari_f
-
-    return {
-        "schema": _JSON_CLUSTERING_REPORT_SCHEMA,
-        "hyperparameters": {
-            "min_cluster_size": int(report.hyperparameters.min_cluster_size),
-        },
-        "min_cluster_size_provenance": (
-            None
-            if report.min_cluster_size_provenance is None
-            else report.min_cluster_size_provenance.to_jsonable()
-        ),
-        "distillation_fidelity": (
-            None
-            if report.distillation_fidelity is None
-            else report.distillation_fidelity.to_jsonable()
-        ),
-        "n_rows_realized": (
-            None if report.n_rows_realized is None else int(report.n_rows_realized)
-        ),
-        "best_score": _json_normalize(float(report.best_score)),
-        "number_properties": int(report.number_properties),
-        "n_clusters_emergent": int(report.n_clusters_emergent),
-        "metrics_df": _dataframe_to_jsonable_records(report.metrics_df),
-        "assignments": _dataframe_to_jsonable_records(report.assignments),
-        "pca_residuals": _ndarray_to_jsonable_nested(report.pca_residuals),
-        "pca_mahalanobis": _ndarray_to_jsonable_nested(report.pca_mahalanobis),
-        "pca_spectral_entropy": _ndarray_to_jsonable_nested(
-            report.pca_spectral_entropy
-        ),
-        "oov_label": _ndarray_to_jsonable_nested(report.oov_label),
-        "umap_clustering": _ndarray_to_jsonable_nested(report.umap_clustering),
-        "cluster_viz": _ndarray_to_jsonable_nested(report.cluster_viz),
-        "cluster_viz_method": str(report.cluster_viz_method),
-        "pca_reduced": _ndarray_to_jsonable_nested(report.pca_reduced),
-        "ari": ari_out,
-        "all_screener_cv": (
-            None
-            if report.all_screener_cv is None
-            else _json_normalize(_all_screener_cv_to_jsonable(report.all_screener_cv))
-        ),
-        "training_diagnostics": (
-            None
-            if report.training_diagnostics is None
-            else _json_normalize(
-                _linker_fit_diagnostics_to_jsonable(report.training_diagnostics)
-            )
-        ),
-    }
-
-
-def write_clustering_report_json(
-    path: str | pathlib.Path, report: ModelSelectionReport, *, indent: int = 2
-) -> None:
-    """
-    Serialize ``report`` with :func:`clustering_report_to_jsonable_dict` to UTF-8 JSON.
-
-    Parent directories are created when missing.
-    """
-    p = pathlib.Path(path).expanduser()
-    p.parent.mkdir(parents=True, exist_ok=True)
-    payload = clustering_report_to_jsonable_dict(report)
-
-    with gzip.open(p, mode="wt", encoding="utf-8", compresslevel=9) as f:
-        json.dump(payload, f, indent=indent)
-
-
-def read_clustering_report_json(path: str | pathlib.Path) -> ModelSelectionReport:
-    """
-    Load a :class:`ModelSelectionReport` written by :func:`write_clustering_report_json`.
-
-    Supports schema ``pelinker.clustering_report.v10`` (cluster-space viz coords).
-    """
-    p = pathlib.Path(path).expanduser()
-    with gzip.open(p, mode="rt", encoding="utf-8") as f:
-        raw: dict[str, Any] = json.load(f)
-
-    schema = str(raw.get("schema", ""))
-    if schema != "pelinker.clustering_report.v10":
-        raise ValueError(f"Unsupported clustering report schema: {schema!r}")
-
-    hp = raw["hyperparameters"]
-    h = ClusteringHyperparameters(min_cluster_size=int(hp["min_cluster_size"]))
-    metrics_df = pd.DataFrame(raw["metrics_df"])
-    assignments = pd.DataFrame(raw["assignments"])
-
-    def _farr(key: str) -> np.ndarray:
-        return np.asarray(raw[key], dtype=np.float64)
-
-    def _iarr(key: str) -> np.ndarray:
-        return np.asarray(raw[key], dtype=np.int64)
-
-    ari_raw = raw.get("ari")
-    ari: float | None
-    if ari_raw is None:
-        ari = None
-    else:
-        ari = float(ari_raw)
-
-    # Nested CV summaries are not round-tripped here (linker fit reports use ``None``).
-    all_cv: AllScreenerCvResult | None = None
-
-    td_raw = raw.get("training_diagnostics")
-    training_diagnostics = _linker_fit_diagnostics_from_jsonable(td_raw)
-
-    return ModelSelectionReport(
-        hyperparameters=h,
-        best_score=float(raw["best_score"]),
-        number_properties=int(raw["number_properties"]),
-        n_clusters_emergent=int(raw["n_clusters_emergent"]),
-        metrics_df=metrics_df,
-        assignments=assignments,
-        pca_residuals=_farr("pca_residuals"),
-        pca_mahalanobis=_farr("pca_mahalanobis"),
-        pca_spectral_entropy=_farr("pca_spectral_entropy"),
-        oov_label=_iarr("oov_label"),
-        umap_clustering=np.asarray(raw["umap_clustering"], dtype=np.float64),
-        cluster_viz=np.asarray(raw["cluster_viz"], dtype=np.float64),
-        cluster_viz_method=str(raw["cluster_viz_method"]),
-        pca_reduced=np.asarray(raw["pca_reduced"], dtype=np.float64),
-        all_screener_cv=all_cv,
-        screener_oos_datapoints=None,
-        ari=ari,
-        training_diagnostics=training_diagnostics,
-    )
-
-
-def _binary_metrics_into_row(
-    row: dict[str, str | float | None],
-    metrics: BinaryClassifierMetrics,
-    prefix: str,
-) -> None:
-    row[f"{prefix}_precision_mean"] = metrics.precision.mean
-    row[f"{prefix}_precision_std"] = metrics.precision.std
-    row[f"{prefix}_recall_mean"] = metrics.recall.mean
-    row[f"{prefix}_recall_std"] = metrics.recall.std
-    row[f"{prefix}_f1_mean"] = metrics.f1.mean
-    row[f"{prefix}_f1_std"] = metrics.f1.std
-    row[f"{prefix}_auc_mean"] = metrics.auc.mean
-    row[f"{prefix}_auc_std"] = metrics.auc.std
-
-
-def _binary_metrics_from_flat_row(
-    row: dict[str, str | float | None],
-    prefix: str,
-) -> BinaryClassifierMetrics:
-    """Inverse of :func:`_binary_metrics_into_row` / JSON branch of :func:`_binary_metrics_to_jsonable`."""
-
-    def _m(field: str) -> MetricMeanStd:
-        mk = f"{prefix}_{field}_mean"
-        sk = f"{prefix}_{field}_std"
-        return MetricMeanStd(
-            mean=float(row[mk]),
-            std=float(row.get(sk) or 0.0),
-        )
-
-    return BinaryClassifierMetrics(
-        precision=_m("precision"),
-        recall=_m("recall"),
-        f1=_m("f1"),
-        auc=_m("auc"),
-    )
-
-
-def _all_screener_cv_from_flat_row(
-    row: dict[str, str | float | None],
-) -> AllScreenerCvResult | None:
-    """Reconstruct unified CV summary from flat CSV/checkpoint rows."""
-    if "screener_lda_precision_mean" not in row or "screener_precision_mean" not in row:
-        return None
-    sb = row.get("screener_best_kind")
-    ow = row.get("oov_winner_kind")
-    if (
-        not isinstance(sb, str)
-        or not sb
-        or not isinstance(ow, str)
-        or not ow
-        or "combined_precision_mean" not in row
-    ):
-        return None
-    return AllScreenerCvResult(
-        screener_lda=_binary_metrics_from_flat_row(row, "screener_lda"),
-        screener_svm=_binary_metrics_from_flat_row(row, "screener_svm"),
-        screener_best_kind=sb,
-        screener_best=_binary_metrics_from_flat_row(row, "screener"),
-        oov_winner_kind=ow,
-        oov=_binary_metrics_from_flat_row(row, "oov"),
-        combined=_binary_metrics_from_flat_row(row, "combined"),
-    )
-
-
-@dataclass(frozen=True)
-class ClusteringSearchSummaryRow:
-    """
-    One row of the model×layer clustering search table (singleton or fusion label).
-
-    Use :meth:`to_flat_dict` for CSV / pandas / heatmaps (legacy column names).
-    """
-
-    model: str
-    layer: str
-    hyperparameters: HyperparameterSearchStats
-    number_properties: MeanWithUncertainty
-    n_clusters_emergent: MeanWithUncertainty
-    dbcv: MeanWithUncertainty
-    ari: MeanWithUncertainty | None
-    all_screener_cv: AllScreenerCvResult | None = None
-    n_rows_realized: MeanWithUncertainty | None = None
-    """Mean (and std) realized mention-row count across this combination's draws.
-
-    ``best_size`` is only interpretable against this scale; carrying it here is what lets
-    :mod:`pelinker.scaling` fit ``min_cluster_size*(N)`` across runs.
-    """
-
-    def to_flat_dict(self) -> dict[str, str | float | None]:
-        """Keys aligned with grid CSV / checkpoint / ``plot_heatmap`` expectations."""
-        h = self.hyperparameters.min_cluster_size
-        p = self.number_properties
-        k = self.n_clusters_emergent
-        d = self.dbcv
-        row: dict[str, str | float | None] = {
-            "model": self.model,
-            "layer": self.layer,
-            "best_size": h.mean,
-            "best_size_std": h.std,
-            "number_properties": p.mean,
-            "number_properties_std": p.std,
-            "n_clusters_emergent": k.mean,
-            "n_clusters_emergent_std": k.std,
-            "best_score": d.mean,
-            "best_score_std": d.std,
-        }
-        nrr = self.n_rows_realized
-        row["n_rows_realized"] = None if nrr is None else nrr.mean
-        row["n_rows_realized_std"] = 0.0 if nrr is None else nrr.std
-        if self.ari is None:
-            row["ari"] = None
-            row["ari_std"] = 0.0
-        else:
-            ari = self.ari
-            row["ari"] = ari.mean
-            row["ari_std"] = ari.std
-        acv = self.all_screener_cv
-        if acv is not None:
-            row["screener_best_kind"] = acv.screener_best_kind
-            _binary_metrics_into_row(row, acv.screener_best, "screener")
-            row["oov_winner_kind"] = acv.oov_winner_kind
-            _binary_metrics_into_row(row, acv.oov, "oov")
-            _binary_metrics_into_row(row, acv.combined, "combined")
-            _binary_metrics_into_row(row, acv.screener_lda, "screener_lda")
-            _binary_metrics_into_row(row, acv.screener_svm, "screener_svm")
-        return row
-
-
-def clustering_search_summary_row_from_flat_dict(
-    row: dict[str, str | float | None],
-) -> ClusteringSearchSummaryRow:
-    """Reconstruct :class:`ClusteringSearchSummaryRow` from :meth:`to_flat_dict` output."""
-    ari_raw = row.get("ari")
-    ari_block: MeanWithUncertainty | None
-    if ari_raw is None or (isinstance(ari_raw, float) and math.isnan(ari_raw)):
-        ari_block = None
-    else:
-        ari_block = MeanWithUncertainty(
-            mean=float(ari_raw),
-            std=float(row.get("ari_std") or 0.0),
-        )
-    # Absent in checkpoints written before n_rows_realized existed; stays None there.
-    nrr_raw = row.get("n_rows_realized")
-    nrr_block: MeanWithUncertainty | None
-    if nrr_raw is None or (isinstance(nrr_raw, float) and math.isnan(nrr_raw)):
-        nrr_block = None
-    else:
-        nrr_block = MeanWithUncertainty(
-            mean=float(nrr_raw),
-            std=float(row.get("n_rows_realized_std") or 0.0),
-        )
-    return ClusteringSearchSummaryRow(
-        model=str(row["model"]),
-        layer=str(row["layer"]),
-        hyperparameters=HyperparameterSearchStats(
-            min_cluster_size=MeanWithUncertainty(
-                mean=float(row["best_size"]),
-                std=float(row["best_size_std"] or 0.0),
-            ),
-        ),
-        number_properties=MeanWithUncertainty(
-            mean=float(row["number_properties"]),
-            std=float(row["number_properties_std"] or 0.0),
-        ),
-        n_clusters_emergent=MeanWithUncertainty(
-            mean=float(row["n_clusters_emergent"]),
-            std=float(row["n_clusters_emergent_std"] or 0.0),
-        ),
-        dbcv=MeanWithUncertainty(
-            mean=float(row["best_score"]),
-            std=float(row["best_score_std"] or 0.0),
-        ),
-        ari=ari_block,
-        all_screener_cv=_all_screener_cv_from_flat_row(row),
-        n_rows_realized=nrr_block,
-    )
-
-
-def summarize_clustering_reports_for_search(
-    reports: Sequence[ModelSelectionReport],
-    *,
-    model: str,
-    layer: str,
-    pooled_min_cluster_size: int | None = None,
-) -> ClusteringSearchSummaryRow:
-    """
-    Aggregate repeated :class:`ClusteringReport` runs into one search summary row.
-
-    When ``pooled_min_cluster_size`` is set (after aggregating grid curves across samples),
-    ``best_size`` / ``best_size_std`` report that single consensus hyperparameter (std is 0)
-    and ``dbcv`` is the mean (and std) of each sample's DBCV **at that grid point**.
-    ``n_clusters_emergent`` remains the mean of per-sample fits at each sample's grid-optimal
-    size; use :func:`n_clusters_at_min_cluster_size` on pooled grid CSV rows for a fixed MCS.
-
-    Otherwise (independent runs or legacy callers) ``best_size`` is the mean of per-report
-    chosen sizes and ``dbcv`` is the mean of each report's ``best_score``.
-
-    Raises:
-        ValueError: if ``reports`` is empty.
-    """
-    if not reports:
-        raise ValueError("reports must be non-empty")
-
-    sizes = np.array(
-        [r.hyperparameters.min_cluster_size for r in reports], dtype=np.float64
-    )
-    scores = np.array([r.best_score for r in reports], dtype=np.float64)
-    nprops = np.array([r.number_properties for r in reports], dtype=np.float64)
-    n_clusters = np.array([r.n_clusters_emergent for r in reports], dtype=np.float64)
-    ari_vals = [float(r.ari) for r in reports if r.ari is not None]
-
-    n = len(reports)
-    std_nprops = float(np.std(nprops)) if n > 1 else 0.0
-    std_n_clusters = float(np.std(n_clusters)) if n > 1 else 0.0
-
-    if pooled_min_cluster_size is not None:
-        sizes_mean = float(pooled_min_cluster_size)
-        std_sizes = 0.0
-        dbcv_at: list[float] = []
-        for r in reports:
-            m = r.metrics_df
-            hit = m.loc[m["min_cluster_size"] == pooled_min_cluster_size, "dbcv"]
-            if len(hit) > 0:
-                dbcv_at.append(float(hit.iloc[0]))
-        if dbcv_at:
-            arr_dbcv = np.array(dbcv_at, dtype=np.float64)
-            dbcv_mean = float(np.mean(arr_dbcv))
-            dbcv_std = float(np.std(arr_dbcv)) if len(arr_dbcv) > 1 else 0.0
-        else:
-            dbcv_mean = float(np.mean(scores))
-            dbcv_std = float(np.std(scores)) if n > 1 else 0.0
-    else:
-        sizes_mean = float(np.mean(sizes))
-        std_sizes = float(np.std(sizes)) if n > 1 else 0.0
-        dbcv_mean = float(np.mean(scores))
-        dbcv_std = float(np.std(scores)) if n > 1 else 0.0
-
-    ari_block: MeanWithUncertainty | None
-    if ari_vals:
-        arr = np.array(ari_vals, dtype=np.float64)
-        ari_block = MeanWithUncertainty(
-            mean=float(np.mean(arr)),
-            std=float(np.std(arr)) if len(arr) > 1 else 0.0,
-        )
-    else:
-        ari_block = None
-
-    acv_reports = [r.all_screener_cv for r in reports if r.all_screener_cv is not None]
-    pooled_acv = _pool_all_screener_cv_results(acv_reports) if acv_reports else None
-
-    rows_realized = [
-        float(r.n_rows_realized) for r in reports if r.n_rows_realized is not None
-    ]
-    n_rows_block: MeanWithUncertainty | None = None
-    if rows_realized:
-        arr_rows = np.array(rows_realized, dtype=np.float64)
-        n_rows_block = MeanWithUncertainty(
-            mean=float(np.mean(arr_rows)),
-            std=float(np.std(arr_rows)) if len(arr_rows) > 1 else 0.0,
-        )
-
-    return ClusteringSearchSummaryRow(
-        model=model,
-        layer=layer,
-        hyperparameters=HyperparameterSearchStats(
-            min_cluster_size=MeanWithUncertainty(
-                mean=sizes_mean,
-                std=std_sizes,
-            ),
-        ),
-        number_properties=MeanWithUncertainty(
-            mean=float(np.mean(nprops)),
-            std=std_nprops,
-        ),
-        n_clusters_emergent=MeanWithUncertainty(
-            mean=float(np.mean(n_clusters)),
-            std=std_n_clusters,
-        ),
-        dbcv=MeanWithUncertainty(
-            mean=dbcv_mean,
-            std=dbcv_std,
-        ),
-        ari=ari_block,
-        all_screener_cv=pooled_acv,
-        n_rows_realized=n_rows_block,
-    )
-
-
-@dataclass(frozen=True)
-class ModelSelectionRunReport:
-    """Standardized aggregate report for one model-selection run."""
-
-    schema: str
-    generated_at: str
-    run_fingerprint: str
-    run_config: dict[str, Any]
-    checkpoint: dict[str, Any]
-    combinations: list[dict[str, Any]]
-    failures: list[dict[str, Any]]
-    best_overall: dict[str, Any] | None
-    best_per_model: dict[str, float]
-
-
-def model_selection_run_report_to_jsonable_dict(
-    report: ModelSelectionRunReport,
-) -> dict[str, Any]:
-    return cast(
-        dict[str, Any],
-        _json_normalize(
-            {
-                "schema": report.schema,
-                "generated_at": report.generated_at,
-                "run_fingerprint": report.run_fingerprint,
-                "run_config": report.run_config,
-                "checkpoint": report.checkpoint,
-                "combinations": report.combinations,
-                "failures": report.failures,
-                "best_overall": report.best_overall,
-                "best_per_model": report.best_per_model,
-            }
-        ),
-    )
-
-
-def write_model_selection_run_report_json(
-    path: str | pathlib.Path,
-    report: ModelSelectionRunReport,
-    *,
-    indent: int = 2,
-) -> None:
-    p = pathlib.Path(path).expanduser()
-    p.parent.mkdir(parents=True, exist_ok=True)
-    payload = model_selection_run_report_to_jsonable_dict(report)
-    with gzip.open(p, mode="wt", encoding="utf-8", compresslevel=9) as f:
-        json.dump(payload, f, indent=indent)
diff --git a/pelinker/reports/__init__.py b/pelinker/reports/__init__.py
new file mode 100644
index 0000000..57e2e44
--- /dev/null
+++ b/pelinker/reports/__init__.py
@@ -0,0 +1 @@
+"""Report schemas, canonical paths, JSON read/write, and flat-row summaries."""
diff --git a/pelinker/reports/io.py b/pelinker/reports/io.py
new file mode 100644
index 0000000..7b24ed3
--- /dev/null
+++ b/pelinker/reports/io.py
@@ -0,0 +1,400 @@
+"""JSON serialization for reports: normalisation, readers and writers."""
+
+from __future__ import annotations
+
+import gzip
+import json
+import logging
+import math
+import pathlib
+from typing import Any, cast
+
+import numpy as np
+import pandas as pd
+
+
+from pelinker.reports.paths import (
+    _FIT_CLUSTER_COMPOSITION_SCHEMA,
+    _JSON_CLUSTERING_REPORT_SCHEMA,
+    _KB_OUT_SCHEMA,
+)
+from pelinker.reports.schema import (
+    AllScreenerCvResult,
+    BinaryClassifierMetrics,
+    ClusteringHyperparameters,
+    LinkerFitDiagnostics,
+    ModelSelectionReport,
+    ModelSelectionRunReport,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _binary_metrics_to_jsonable(
+    m: BinaryClassifierMetrics,
+) -> dict[str, dict[str, float]]:
+    return {
+        "precision": {"mean": m.precision.mean, "std": m.precision.std},
+        "recall": {"mean": m.recall.mean, "std": m.recall.std},
+        "f1": {"mean": m.f1.mean, "std": m.f1.std},
+        "auc": {"mean": m.auc.mean, "std": m.auc.std},
+    }
+
+
+def _all_screener_cv_to_jsonable(result: AllScreenerCvResult) -> dict[str, object]:
+    return {
+        "screener_lda": _binary_metrics_to_jsonable(result.screener_lda),
+        "screener_svm": _binary_metrics_to_jsonable(result.screener_svm),
+        "screener_best_kind": result.screener_best_kind,
+        "screener_best": _binary_metrics_to_jsonable(result.screener_best),
+        "oov_winner_kind": result.oov_winner_kind,
+        "oov": _binary_metrics_to_jsonable(result.oov),
+        "combined": _binary_metrics_to_jsonable(result.combined),
+    }
+
+
+def _json_normalize(obj: object) -> object:
+    """Map values to types accepted by :func:`json.dumps` (no NaN/Inf; no numpy scalars)."""
+    if obj is None or isinstance(obj, (str, bool)):
+        return obj
+    if isinstance(obj, (float, np.floating)):
+        x = float(obj)
+        if math.isnan(x) or math.isinf(x):
+            return None
+        return x
+    if isinstance(obj, (int, np.integer)):
+        return int(obj)
+    if isinstance(obj, dict):
+        return {str(k): _json_normalize(v) for k, v in obj.items()}
+    if isinstance(obj, (list, tuple)):
+        return [_json_normalize(x) for x in obj]
+    logger.warning(
+        "json_normalize: coercing unexpected type %s to str", type(obj).__name__
+    )
+    return str(obj)
+
+
+def _dataframe_to_jsonable_records(df: pd.DataFrame) -> list[dict[str, Any]]:
+    return [
+        {str(k): _json_normalize(v) for k, v in row.items()}
+        for row in df.to_dict(orient="records")
+    ]
+
+
+def _ndarray_to_jsonable_nested(arr: np.ndarray) -> Any:
+    return _json_normalize(np.asarray(arr).tolist())
+
+
+def write_cluster_composition_json(
+    path: str | pathlib.Path,
+    composition_df: pd.DataFrame,
+    *,
+    top_n: int = 3,
+    weighting: str = "inv_sqrt_mention_count",
+    exclude_noise: bool = False,
+    summary: dict[str, Any] | None = None,
+    max_clusters_in_rows: int | None = None,
+    indent: int = 2,
+) -> None:
+    """Serialize a processed cluster-composition table written at fit time."""
+    from pelinker.clustering.composition import ENTITY_WEIGHTING_INV_SQRT
+
+    p = pathlib.Path(path).expanduser()
+    p.parent.mkdir(parents=True, exist_ok=True)
+    payload: dict[str, Any] = {
+        "schema": _FIT_CLUSTER_COMPOSITION_SCHEMA,
+        "top_n": int(top_n),
+        "weighting": weighting or ENTITY_WEIGHTING_INV_SQRT,
+        "exclude_noise": bool(exclude_noise),
+        "rows": _dataframe_to_jsonable_records(composition_df),
+    }
+    if summary is not None:
+        payload["summary"] = _json_normalize(summary)
+    if max_clusters_in_rows is not None:
+        payload["max_clusters_in_rows"] = int(max_clusters_in_rows)
+    with gzip.open(p, mode="wt", encoding="utf-8", compresslevel=9) as f:
+        json.dump(payload, f, indent=indent)
+
+
+def read_cluster_composition_json(
+    path: str | pathlib.Path,
+) -> tuple[pd.DataFrame, dict[str, Any]]:
+    """Load a composition table and metadata written by :func:`write_cluster_composition_json`."""
+    p = pathlib.Path(path).expanduser()
+    with gzip.open(p, mode="rt", encoding="utf-8") as f:
+        raw: dict[str, Any] = json.load(f)
+    schema = str(raw.get("schema", ""))
+    if schema not in (
+        "pelinker.fit_cluster_composition.v1",
+        _FIT_CLUSTER_COMPOSITION_SCHEMA,
+    ):
+        raise ValueError(f"Unsupported cluster composition schema: {schema!r}")
+    meta = {k: v for k, v in raw.items() if k != "rows"}
+    return pd.DataFrame(raw["rows"]), meta
+
+
+def write_kb_out_json(
+    path: str | pathlib.Path,
+    payload: dict[str, Any],
+    *,
+    indent: int = 2,
+) -> None:
+    """Write :func:`~pelinker.kb.kb_out.build_kb_out_catalog` output."""
+    p = pathlib.Path(path).expanduser()
+    p.parent.mkdir(parents=True, exist_ok=True)
+    if str(payload.get("schema", "")) != _KB_OUT_SCHEMA:
+        raise ValueError(
+            f"Expected schema {_KB_OUT_SCHEMA!r}, got {payload.get('schema')!r}"
+        )
+    with p.open("w", encoding="utf-8") as f:
+        json.dump(_json_normalize(payload), f, indent=indent, ensure_ascii=False)
+
+
+def read_kb_out_json(path: str | pathlib.Path) -> dict[str, Any]:
+    """Load KB-out catalog JSON."""
+    p = pathlib.Path(path).expanduser()
+    with p.open(encoding="utf-8") as f:
+        raw: dict[str, Any] = json.load(f)
+    if str(raw.get("schema", "")) != _KB_OUT_SCHEMA:
+        raise ValueError(f"Unsupported KB-out schema: {raw.get('schema')!r}")
+    return raw
+
+
+def write_model_selection_summary_json(
+    path: str | pathlib.Path,
+    payload: dict[str, Any],
+    *,
+    indent: int = 2,
+) -> None:
+    p = pathlib.Path(path).expanduser()
+    p.parent.mkdir(parents=True, exist_ok=True)
+    body = cast(dict[str, Any], _json_normalize(payload))
+    with p.open("w", encoding="utf-8") as f:
+        json.dump(body, f, indent=indent, ensure_ascii=False)
+
+
+def _linker_fit_diagnostics_to_jsonable(d: LinkerFitDiagnostics) -> dict[str, Any]:
+    return {
+        "pca_residual": _ndarray_to_jsonable_nested(d.pca_residual),
+        "pca_mahalanobis": _ndarray_to_jsonable_nested(d.pca_mahalanobis),
+        "pca_spectral_entropy": _ndarray_to_jsonable_nested(d.pca_spectral_entropy),
+        "oov_label": _ndarray_to_jsonable_nested(d.oov_label),
+        "screener_decision": _ndarray_to_jsonable_nested(d.screener_decision),
+        "projection_score": _ndarray_to_jsonable_nested(d.projection_score),
+        "n_total": int(d.n_total),
+        "sample_random_state": int(d.sample_random_state),
+    }
+
+
+def _linker_fit_diagnostics_from_jsonable(obj: object) -> LinkerFitDiagnostics | None:
+    if obj is None or not isinstance(obj, dict):
+        return None
+    d = cast(dict[str, Any], obj)
+    needed = (
+        "pca_residual",
+        "pca_mahalanobis",
+        "pca_spectral_entropy",
+        "oov_label",
+        "screener_decision",
+        "projection_score",
+        "n_total",
+        "sample_random_state",
+    )
+    if not all(k in d for k in needed):
+        return None
+    pr = np.asarray(d["pca_residual"], dtype=np.float64)
+    pm = np.asarray(d["pca_mahalanobis"], dtype=np.float64)
+    pe = np.asarray(d["pca_spectral_entropy"], dtype=np.float64)
+    ol = np.asarray(d["oov_label"], dtype=np.int64).ravel()
+    sd = np.asarray(d["screener_decision"], dtype=np.float64).ravel()
+    mo = np.asarray(d["projection_score"], dtype=np.float64).ravel()
+    n_tot = int(d["n_total"])
+    srs = int(d["sample_random_state"])
+    m = len(pr)
+    if not (
+        len(pm) == m and len(pe) == m and len(ol) == m and len(sd) == m and len(mo) == m
+    ):
+        return None
+    return LinkerFitDiagnostics(
+        pca_residual=pr,
+        pca_mahalanobis=pm,
+        pca_spectral_entropy=pe,
+        oov_label=ol,
+        screener_decision=sd,
+        projection_score=mo,
+        n_total=n_tot,
+        sample_random_state=srs,
+    )
+
+
+def clustering_report_to_jsonable_dict(report: ModelSelectionReport) -> dict[str, Any]:
+    """
+    Flatten a :class:`ClusteringReport` into JSON-serializable built-ins (no DataFrames/ndarrays).
+
+    Intended for ``json.dumps`` or for pickling a stable, language-adjacent blob. Schema version
+    is stored under ``"schema"`` for forward compatibility.
+    """
+    ari_out: float | None
+    if report.ari is None:
+        ari_out = None
+    else:
+        ari_f = float(report.ari)
+        ari_out = None if math.isnan(ari_f) or math.isinf(ari_f) else ari_f
+
+    return {
+        "schema": _JSON_CLUSTERING_REPORT_SCHEMA,
+        "hyperparameters": {
+            "min_cluster_size": int(report.hyperparameters.min_cluster_size),
+        },
+        "min_cluster_size_provenance": (
+            None
+            if report.min_cluster_size_provenance is None
+            else report.min_cluster_size_provenance.to_jsonable()
+        ),
+        "distillation_fidelity": (
+            None
+            if report.distillation_fidelity is None
+            else report.distillation_fidelity.to_jsonable()
+        ),
+        "n_rows_realized": (
+            None if report.n_rows_realized is None else int(report.n_rows_realized)
+        ),
+        "best_score": _json_normalize(float(report.best_score)),
+        "number_properties": int(report.number_properties),
+        "n_clusters_emergent": int(report.n_clusters_emergent),
+        "metrics_df": _dataframe_to_jsonable_records(report.metrics_df),
+        "assignments": _dataframe_to_jsonable_records(report.assignments),
+        "pca_residuals": _ndarray_to_jsonable_nested(report.pca_residuals),
+        "pca_mahalanobis": _ndarray_to_jsonable_nested(report.pca_mahalanobis),
+        "pca_spectral_entropy": _ndarray_to_jsonable_nested(
+            report.pca_spectral_entropy
+        ),
+        "oov_label": _ndarray_to_jsonable_nested(report.oov_label),
+        "umap_clustering": _ndarray_to_jsonable_nested(report.umap_clustering),
+        "cluster_viz": _ndarray_to_jsonable_nested(report.cluster_viz),
+        "cluster_viz_method": str(report.cluster_viz_method),
+        "pca_reduced": _ndarray_to_jsonable_nested(report.pca_reduced),
+        "ari": ari_out,
+        "all_screener_cv": (
+            None
+            if report.all_screener_cv is None
+            else _json_normalize(_all_screener_cv_to_jsonable(report.all_screener_cv))
+        ),
+        "training_diagnostics": (
+            None
+            if report.training_diagnostics is None
+            else _json_normalize(
+                _linker_fit_diagnostics_to_jsonable(report.training_diagnostics)
+            )
+        ),
+    }
+
+
+def write_clustering_report_json(
+    path: str | pathlib.Path, report: ModelSelectionReport, *, indent: int = 2
+) -> None:
+    """
+    Serialize ``report`` with :func:`clustering_report_to_jsonable_dict` to UTF-8 JSON.
+
+    Parent directories are created when missing.
+    """
+    p = pathlib.Path(path).expanduser()
+    p.parent.mkdir(parents=True, exist_ok=True)
+    payload = clustering_report_to_jsonable_dict(report)
+
+    with gzip.open(p, mode="wt", encoding="utf-8", compresslevel=9) as f:
+        json.dump(payload, f, indent=indent)
+
+
+def read_clustering_report_json(path: str | pathlib.Path) -> ModelSelectionReport:
+    """
+    Load a :class:`ModelSelectionReport` written by :func:`write_clustering_report_json`.
+
+    Supports schema ``pelinker.clustering_report.v10`` (cluster-space viz coords).
+    """
+    p = pathlib.Path(path).expanduser()
+    with gzip.open(p, mode="rt", encoding="utf-8") as f:
+        raw: dict[str, Any] = json.load(f)
+
+    schema = str(raw.get("schema", ""))
+    if schema != "pelinker.clustering_report.v10":
+        raise ValueError(f"Unsupported clustering report schema: {schema!r}")
+
+    hp = raw["hyperparameters"]
+    h = ClusteringHyperparameters(min_cluster_size=int(hp["min_cluster_size"]))
+    metrics_df = pd.DataFrame(raw["metrics_df"])
+    assignments = pd.DataFrame(raw["assignments"])
+
+    def _farr(key: str) -> np.ndarray:
+        return np.asarray(raw[key], dtype=np.float64)
+
+    def _iarr(key: str) -> np.ndarray:
+        return np.asarray(raw[key], dtype=np.int64)
+
+    ari_raw = raw.get("ari")
+    ari: float | None
+    if ari_raw is None:
+        ari = None
+    else:
+        ari = float(ari_raw)
+
+    # Nested CV summaries are not round-tripped here (linker fit reports use ``None``).
+    all_cv: AllScreenerCvResult | None = None
+
+    td_raw = raw.get("training_diagnostics")
+    training_diagnostics = _linker_fit_diagnostics_from_jsonable(td_raw)
+
+    return ModelSelectionReport(
+        hyperparameters=h,
+        best_score=float(raw["best_score"]),
+        number_properties=int(raw["number_properties"]),
+        n_clusters_emergent=int(raw["n_clusters_emergent"]),
+        metrics_df=metrics_df,
+        assignments=assignments,
+        pca_residuals=_farr("pca_residuals"),
+        pca_mahalanobis=_farr("pca_mahalanobis"),
+        pca_spectral_entropy=_farr("pca_spectral_entropy"),
+        oov_label=_iarr("oov_label"),
+        umap_clustering=np.asarray(raw["umap_clustering"], dtype=np.float64),
+        cluster_viz=np.asarray(raw["cluster_viz"], dtype=np.float64),
+        cluster_viz_method=str(raw["cluster_viz_method"]),
+        pca_reduced=np.asarray(raw["pca_reduced"], dtype=np.float64),
+        all_screener_cv=all_cv,
+        screener_oos_datapoints=None,
+        ari=ari,
+        training_diagnostics=training_diagnostics,
+    )
+
+
+def model_selection_run_report_to_jsonable_dict(
+    report: ModelSelectionRunReport,
+) -> dict[str, Any]:
+    return cast(
+        dict[str, Any],
+        _json_normalize(
+            {
+                "schema": report.schema,
+                "generated_at": report.generated_at,
+                "run_fingerprint": report.run_fingerprint,
+                "run_config": report.run_config,
+                "checkpoint": report.checkpoint,
+                "combinations": report.combinations,
+                "failures": report.failures,
+                "best_overall": report.best_overall,
+                "best_per_model": report.best_per_model,
+            }
+        ),
+    )
+
+
+def write_model_selection_run_report_json(
+    path: str | pathlib.Path,
+    report: ModelSelectionRunReport,
+    *,
+    indent: int = 2,
+) -> None:
+    p = pathlib.Path(path).expanduser()
+    p.parent.mkdir(parents=True, exist_ok=True)
+    payload = model_selection_run_report_to_jsonable_dict(report)
+    with gzip.open(p, mode="wt", encoding="utf-8", compresslevel=9) as f:
+        json.dump(payload, f, indent=indent)
diff --git a/pelinker/reports/paths.py b/pelinker/reports/paths.py
new file mode 100644
index 0000000..259366b
--- /dev/null
+++ b/pelinker/reports/paths.py
@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+import pathlib
+
+
+"""Report filenames, schema identifiers and path builders."""
+
+_JSON_CLUSTERING_REPORT_SCHEMA = "pelinker.clustering_report.v10"
+
+MODEL_SELECTION_RUN_REPORT_SCHEMA = "pelinker.search.model_selection.run_report.v2"
+
+# Basenames for artifacts under one report directory (``pelinker-fit`` / clustering search).
+LINKER_FIT_CLUSTERING_REPORT_BASENAME = "linker_fit.clustering_report.json.gz"
+
+LINKER_FIT_CLUSTER_COMPOSITION_BASENAME = "linker_fit.cluster_composition.json.gz"
+
+LINKER_FIT_KB_OUT_BASENAME = "linker_fit.kb_out.json"
+
+_FIT_CLUSTER_COMPOSITION_SCHEMA = "pelinker.fit_cluster_composition.v2"
+
+_KB_OUT_SCHEMA = "pelinker.kb.kb_out.v1"
+
+MODEL_SELECTION_RUN_REPORT_BASENAME = "model_selection.run_report.json.gz"
+
+MODEL_SELECTION_SUMMARY_JSON_SCHEMA = "pelinker.search.model_selection.summary.v1"
+
+MODEL_SELECTION_SUMMARY_JSON_BASENAME = "model_selection.summary.json"
+
+CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME = "results_grid_per_sample.csv"
+
+CLUSTERING_SEARCH_GRID_CHOSEN_JSON_BASENAME = "grid_chosen_hyperparameters.json"
+
+CLUSTERING_SEARCH_FINE_METADATA_BASENAME = "fine_clustering_metadata.jsonl.gz"
+
+FINE_SCREENER_EVAL_BASENAME = "fine_screener_eval.jsonl.gz"
+
+CLUSTERING_SEARCH_SAMPLE_LABELS_BASENAME = "sample_cluster_labels.parquet"
+"""Per-bootstrap cluster assignments, written only when label persistence is requested.
+
+Needed to measure cluster *identity* stability across draws
+(:mod:`pelinker.clustering.stability`); the grid CSVs carry metrics per draw but no
+labels, so without this the reported cluster-count spread cannot be told apart from
+wholesale membership churn."""
+
+MODEL_SELECTION_CHECKPOINT_BASENAME = "model_selection.state.json.gz"
+
+
+def linker_fit_clustering_report_path(report_dir: str | pathlib.Path) -> pathlib.Path:
+    """Filesystem path for the fit-time :class:`ClusteringReport` JSON under ``report_dir``."""
+    return pathlib.Path(report_dir).expanduser() / LINKER_FIT_CLUSTERING_REPORT_BASENAME
+
+
+def linker_fit_cluster_composition_path(
+    report_dir: str | pathlib.Path,
+) -> pathlib.Path:
+    """Filesystem path for the entity-weighted cluster composition artifact."""
+    return (
+        pathlib.Path(report_dir).expanduser() / LINKER_FIT_CLUSTER_COMPOSITION_BASENAME
+    )
+
+
+def linker_fit_kb_out_path(report_dir: str | pathlib.Path) -> pathlib.Path:
+    """Filesystem path for the KB-out catalog JSON under ``report_dir``."""
+    return pathlib.Path(report_dir).expanduser() / LINKER_FIT_KB_OUT_BASENAME
+
+
+def model_selection_run_report_path(report_dir: str | pathlib.Path) -> pathlib.Path:
+    """Filesystem path for the standardized model-selection aggregate report."""
+    return pathlib.Path(report_dir).expanduser() / MODEL_SELECTION_RUN_REPORT_BASENAME
+
+
+def model_selection_summary_json_path(report_dir: str | pathlib.Path) -> pathlib.Path:
+    """Top-level replot summary (rankings, best combos) as plain JSON."""
+    return pathlib.Path(report_dir).expanduser() / MODEL_SELECTION_SUMMARY_JSON_BASENAME
diff --git a/pelinker/reports/schema.py b/pelinker/reports/schema.py
new file mode 100644
index 0000000..fda8bbb
--- /dev/null
+++ b/pelinker/reports/schema.py
@@ -0,0 +1,439 @@
+from __future__ import annotations
+
+from collections import Counter
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from pelinker.core.config import ScreenerKind
+from pelinker.linker.distillation import DistillationFidelityMetrics
+from pelinker.core.scaling import MinClusterSizeProvenance
+
+"""Report dataclasses: the in-memory shape of every persisted pelinker report."""
+
+
+@dataclass(frozen=True)
+class MeanWithUncertainty:
+    """Sample mean and standard deviation (ddof=1) over repeated runs; ``std=0`` for a single run."""
+
+    mean: float
+    std: float
+
+
+@dataclass(frozen=True)
+class MetricMeanStd:
+    """Mean and spread (sample std over CV folds) for one scalar metric."""
+
+    mean: float
+    std: float
+
+
+@dataclass(frozen=True)
+class BinaryClassifierMetrics:
+    """Precision / recall / F1 / AUC vs negative class (label 1); spread is fold-wise."""
+
+    precision: MetricMeanStd
+    recall: MetricMeanStd
+    f1: MetricMeanStd
+    auc: MetricMeanStd
+
+
+@dataclass(frozen=True)
+class AllScreenerCvResult:
+    """Unified stratified CV: embedding screener (LDA vs SVM), manifold OOV, and stacked score."""
+
+    screener_lda: BinaryClassifierMetrics
+    screener_svm: BinaryClassifierMetrics
+    screener_best_kind: str
+    screener_best: BinaryClassifierMetrics
+    oov_winner_kind: str
+    oov: BinaryClassifierMetrics
+    combined: BinaryClassifierMetrics
+
+
+@dataclass(frozen=True)
+class PerDatapointScores:
+    """Out-of-sample scores per stratified-fold test datapoint."""
+
+    orig_idx: list[int]
+    entity: list[str]
+    y_true: list[int]
+    screener_lda_score: list[float]
+    screener_svm_score: list[float]
+    screener_best_score: list[float]
+    oov_score: list[float]
+    combined_score: list[float]
+
+
+@dataclass(frozen=True)
+class NegativeScreenerInSampleMetrics:
+    """Train-set precision / recall / F1 for detecting ``negative_label`` (binary label 1)."""
+
+    precision: float
+    recall: float
+    f1: float
+    n_kb_mentions: int
+    """Rows whose ``entity`` is not the synthetic negative label (class 0)."""
+    n_negative_label_mentions: int
+    """Rows whose ``entity`` equals the synthetic negative label (class 1)."""
+    kind: ScreenerKind
+
+
+@dataclass(frozen=True)
+class ClusteringFitMetrics:
+    """Fit-time clustering diagnostics at a fixed ``min_cluster_size``."""
+
+    min_cluster_size: int
+    dbcv: float | None
+    """HDBSCAN ``relative_validity_`` when available."""
+    ari: float | None
+    n_clusters_emergent: int
+    noise_fraction: float
+    n_samples: int
+
+
+def _pool_binary_classifier_metrics_branch(
+    summaries: Sequence[AllScreenerCvResult],
+    branch: Callable[[AllScreenerCvResult], BinaryClassifierMetrics],
+) -> BinaryClassifierMetrics:
+    def _collect(field: str) -> MetricMeanStd:
+        vals = np.array(
+            [getattr(branch(s), field).mean for s in summaries],
+            dtype=np.float64,
+        )
+        return MetricMeanStd(
+            mean=float(np.mean(vals)),
+            std=float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0,
+        )
+
+    return BinaryClassifierMetrics(
+        precision=_collect("precision"),
+        recall=_collect("recall"),
+        f1=_collect("f1"),
+        auc=_collect("auc"),
+    )
+
+
+def _pool_all_screener_cv_results(
+    summaries: Sequence[AllScreenerCvResult],
+) -> AllScreenerCvResult:
+    """Pool mean±std across bootstrap samples from each report's CV ``mean`` fields."""
+    if not summaries:
+        raise ValueError("summaries must be non-empty")
+
+    def _mode_str(values: list[str]) -> str:
+        c = Counter(values)
+        return str(c.most_common(1)[0][0])
+
+    kinds_s = [s.screener_best_kind for s in summaries]
+    kinds_o = [s.oov_winner_kind for s in summaries]
+    return AllScreenerCvResult(
+        screener_lda=_pool_binary_classifier_metrics_branch(
+            summaries, lambda r: r.screener_lda
+        ),
+        screener_svm=_pool_binary_classifier_metrics_branch(
+            summaries, lambda r: r.screener_svm
+        ),
+        screener_best_kind=_mode_str(kinds_s),
+        screener_best=_pool_binary_classifier_metrics_branch(
+            summaries, lambda r: r.screener_best
+        ),
+        oov_winner_kind=_mode_str(kinds_o),
+        oov=_pool_binary_classifier_metrics_branch(summaries, lambda r: r.oov),
+        combined=_pool_binary_classifier_metrics_branch(
+            summaries, lambda r: r.combined
+        ),
+    )
+
+
+@dataclass(frozen=True)
+class ClusteringHyperparameters:
+    """
+    HDBSCAN (and related) choices selected by the grid search / smoother.
+
+    Add fields here as more knobs participate in optimization; call sites then stay typed.
+    """
+
+    min_cluster_size: int
+
+
+@dataclass(frozen=True)
+class HyperparameterSearchStats:
+    """Distribution of chosen hyperparameters across repeated clustering samples."""
+
+    min_cluster_size: MeanWithUncertainty
+
+
+@dataclass(frozen=True)
+class LinkerFitDiagnostics:
+    """Per-row training diagnostics for plotting (often stratified-subsampled)."""
+
+    pca_residual: np.ndarray
+    pca_mahalanobis: np.ndarray
+    pca_spectral_entropy: np.ndarray
+    oov_label: np.ndarray
+    """``1`` iff ``entity == negative_label`` (same convention as :class:`ModelSelectionReport`)."""
+    screener_decision: np.ndarray
+    projection_score: np.ndarray
+    n_total: int
+    """Original mention count before subsampling (same as ``len(prepared)`` at fit time)."""
+    sample_random_state: int
+    """RNG seed used for stratified subsampling (or configured seed when no subsample)."""
+
+
+def _copy_linker_fit_diagnostics(
+    full: LinkerFitDiagnostics, random_state: int
+) -> LinkerFitDiagnostics:
+    return LinkerFitDiagnostics(
+        pca_residual=np.asarray(full.pca_residual, dtype=np.float64).copy(),
+        pca_mahalanobis=np.asarray(full.pca_mahalanobis, dtype=np.float64).copy(),
+        pca_spectral_entropy=np.asarray(
+            full.pca_spectral_entropy, dtype=np.float64
+        ).copy(),
+        oov_label=np.asarray(full.oov_label, dtype=np.int64).copy(),
+        screener_decision=np.asarray(full.screener_decision, dtype=np.float64).copy(),
+        projection_score=np.asarray(full.projection_score, dtype=np.float64).copy(),
+        n_total=full.n_total,
+        sample_random_state=random_state,
+    )
+
+
+def _slice_linker_fit_diagnostics(
+    full: LinkerFitDiagnostics,
+    indices: np.ndarray,
+    random_state: int,
+) -> LinkerFitDiagnostics:
+    return LinkerFitDiagnostics(
+        pca_residual=np.asarray(full.pca_residual, dtype=np.float64)[indices],
+        pca_mahalanobis=np.asarray(full.pca_mahalanobis, dtype=np.float64)[indices],
+        pca_spectral_entropy=np.asarray(full.pca_spectral_entropy, dtype=np.float64)[
+            indices
+        ],
+        oov_label=np.asarray(full.oov_label, dtype=np.int64)[indices],
+        screener_decision=np.asarray(full.screener_decision, dtype=np.float64)[indices],
+        projection_score=np.asarray(full.projection_score, dtype=np.float64)[indices],
+        n_total=full.n_total,
+        sample_random_state=random_state,
+    )
+
+
+def subsample_diagnostics_stratified(
+    full: LinkerFitDiagnostics,
+    *,
+    max_rows: int,
+    random_state: int,
+) -> LinkerFitDiagnostics:
+    """
+    Stratified subsample by ``oov_label`` (preserve class proportions, at least one row per
+    non-empty class when two classes exist). Mirrors logic used in pairgrid plotting.
+    """
+    if max_rows < 1:
+        raise ValueError("max_rows must be >= 1")
+    n_total = full.n_total
+    n = int(len(full.pca_residual))
+    if n != n_total:
+        raise ValueError(
+            f"LinkerFitDiagnostics length mismatch: len(arrays)={n} vs n_total={n_total}"
+        )
+
+    def _copy_same() -> LinkerFitDiagnostics:
+        return _copy_linker_fit_diagnostics(full, random_state)
+
+    if n <= max_rows:
+        return _copy_same()
+
+    rng = np.random.default_rng(random_state)
+    y = np.asarray(full.oov_label, dtype=np.int64).ravel()
+    classes = np.unique(y)
+
+    if len(classes) == 1:
+        idx_all = np.flatnonzero(y == int(classes[0]))
+        k = min(max_rows, len(idx_all))
+        chosen = rng.choice(idx_all, size=k, replace=False)
+    else:
+        parts: list[np.ndarray] = []
+        for cls in classes:
+            idx = np.flatnonzero(y == int(cls))
+            k_i = max(1, int(round(max_rows * len(idx) / n)))
+            k_i = min(k_i, len(idx))
+            parts.append(rng.choice(idx, size=k_i, replace=False))
+        chosen = np.concatenate(parts)
+        if len(chosen) > max_rows:
+            chosen = rng.choice(chosen, size=max_rows, replace=False)
+
+    chosen = np.sort(chosen.astype(np.int64, copy=False))
+    return _slice_linker_fit_diagnostics(full, chosen, random_state)
+
+
+@dataclass
+class ModelSelectionReport:
+    """Report containing clustering analysis results for one sample."""
+
+    hyperparameters: ClusteringHyperparameters
+    best_score: float
+    """DBCV (``relative_validity_``) at the chosen ``min_cluster_size`` (mean when from aggregate)."""
+
+    number_properties: int
+    """Count of distinct KB ``entity`` labels in the frame used for PCA→UMAP (excludes ``pelinker.core.onto.NEGATIVE_LABEL`` when screening)."""
+
+    n_clusters_emergent: int
+    """HDBSCAN emergent cluster count at :attr:`hyperparameters` ``min_cluster_size``.
+
+    In model-selection this is the grid-optimal ``best_size``, not an arbitrary MCS.
+    To compare with ``Linker.fit`` at a fixed MCS, use
+    :func:`n_clusters_at_min_cluster_size` on :attr:`metrics_df` instead.
+    """
+
+    metrics_df: pd.DataFrame
+    assignments: pd.DataFrame
+    pca_residuals: np.ndarray
+    pca_mahalanobis: np.ndarray
+    pca_spectral_entropy: np.ndarray
+    oov_label: np.ndarray
+    """Per-row OOV mask: ``1`` iff ``entity == negative_label`` (same length as ``pca_residuals``)."""
+    umap_clustering: np.ndarray
+    cluster_viz: np.ndarray
+    cluster_viz_method: str
+    pca_reduced: np.ndarray
+    all_screener_cv: AllScreenerCvResult | None = None
+    """Unified stratified CV for embedding screener, manifold OOV, and stacked score."""
+    screener_oos_datapoints: PerDatapointScores | None = None
+    """Per-datapoint OOS scores (not serialized in JSON clustering report)."""
+    ari: float | None = None
+    training_diagnostics: LinkerFitDiagnostics | None = None
+    """Stratified-subsampled PCA quality + screener / manifold OOV scores (linker fit only)."""
+    mention_quality: pd.DataFrame | None = None
+    """All mentions (pos+neg) with PCA quality scores and oov_label; cluster=-1 for negatives."""
+    distillation_fidelity: DistillationFidelityMetrics | None = None
+    """Held-out agreement between the compact entity head and its HDBSCAN teacher.
+
+    ``None`` for legacy fits (no student) and when the holdout was disabled or skipped.
+    """
+    min_cluster_size_provenance: MinClusterSizeProvenance | None = None
+    """Where :attr:`hyperparameters` ``min_cluster_size`` came from (linker fit only).
+
+    Explicit, extrapolated from a scale curve, or the bare default — recorded so a fitted
+    model never carries an unexplained hyperparameter.
+    """
+    n_rows_realized: int | None = None
+    """Mention rows this draw actually clustered on.
+
+    The realized N, **not** the ``clustering_sample_rows`` cap: when the frame is smaller
+    than the cap, or the cap is unset, the two differ. Sample-size-dependent
+    hyperparameters (``min_cluster_size``, and through it HDBSCAN ``min_samples``) are
+    only interpretable against this number — see :mod:`pelinker.core.scaling`.
+    """
+
+
+def n_clusters_at_min_cluster_size(
+    metrics_df: pd.DataFrame,
+    min_cluster_size: int,
+) -> int | None:
+    """``n_clusters`` from a grid ``metrics_df`` row at ``min_cluster_size`` (for fit parity)."""
+    if (
+        "min_cluster_size" not in metrics_df.columns
+        or "n_clusters" not in metrics_df.columns
+    ):
+        return None
+    hit = metrics_df.loc[
+        metrics_df["min_cluster_size"] == min_cluster_size, "n_clusters"
+    ]
+    if hit.empty:
+        return None
+    return int(hit.iloc[0])
+
+
+@dataclass(frozen=True)
+class ClusteringSearchSummaryRow:
+    """
+    One row of the model×layer clustering search table (singleton or fusion label).
+
+    Use :meth:`to_flat_dict` for CSV / pandas / heatmaps (legacy column names).
+    """
+
+    model: str
+    layer: str
+    hyperparameters: HyperparameterSearchStats
+    number_properties: MeanWithUncertainty
+    n_clusters_emergent: MeanWithUncertainty
+    dbcv: MeanWithUncertainty
+    ari: MeanWithUncertainty | None
+    all_screener_cv: AllScreenerCvResult | None = None
+    n_rows_realized: MeanWithUncertainty | None = None
+    """Mean (and std) realized mention-row count across this combination's draws.
+
+    ``best_size`` is only interpretable against this scale; carrying it here is what lets
+    :mod:`pelinker.core.scaling` fit ``min_cluster_size*(N)`` across runs.
+    """
+
+    def to_flat_dict(self) -> dict[str, str | float | None]:
+        """Keys aligned with grid CSV / checkpoint / ``plot_heatmap`` expectations."""
+        h = self.hyperparameters.min_cluster_size
+        p = self.number_properties
+        k = self.n_clusters_emergent
+        d = self.dbcv
+        row: dict[str, str | float | None] = {
+            "model": self.model,
+            "layer": self.layer,
+            "best_size": h.mean,
+            "best_size_std": h.std,
+            "number_properties": p.mean,
+            "number_properties_std": p.std,
+            "n_clusters_emergent": k.mean,
+            "n_clusters_emergent_std": k.std,
+            "best_score": d.mean,
+            "best_score_std": d.std,
+        }
+        nrr = self.n_rows_realized
+        row["n_rows_realized"] = None if nrr is None else nrr.mean
+        row["n_rows_realized_std"] = 0.0 if nrr is None else nrr.std
+        if self.ari is None:
+            row["ari"] = None
+            row["ari_std"] = 0.0
+        else:
+            ari = self.ari
+            row["ari"] = ari.mean
+            row["ari_std"] = ari.std
+        acv = self.all_screener_cv
+        if acv is not None:
+            row["screener_best_kind"] = acv.screener_best_kind
+            _binary_metrics_into_row(row, acv.screener_best, "screener")
+            row["oov_winner_kind"] = acv.oov_winner_kind
+            _binary_metrics_into_row(row, acv.oov, "oov")
+            _binary_metrics_into_row(row, acv.combined, "combined")
+            _binary_metrics_into_row(row, acv.screener_lda, "screener_lda")
+            _binary_metrics_into_row(row, acv.screener_svm, "screener_svm")
+        return row
+
+
+@dataclass(frozen=True)
+class ModelSelectionRunReport:
+    """Standardized aggregate report for one model-selection run."""
+
+    schema: str
+    generated_at: str
+    run_fingerprint: str
+    run_config: dict[str, Any]
+    checkpoint: dict[str, Any]
+    combinations: list[dict[str, Any]]
+    failures: list[dict[str, Any]]
+    best_overall: dict[str, Any] | None
+    best_per_model: dict[str, float]
+
+
+def _binary_metrics_into_row(
+    row: dict[str, str | float | None],
+    metrics: BinaryClassifierMetrics,
+    prefix: str,
+) -> None:
+    row[f"{prefix}_precision_mean"] = metrics.precision.mean
+    row[f"{prefix}_precision_std"] = metrics.precision.std
+    row[f"{prefix}_recall_mean"] = metrics.recall.mean
+    row[f"{prefix}_recall_std"] = metrics.recall.std
+    row[f"{prefix}_f1_mean"] = metrics.f1.mean
+    row[f"{prefix}_f1_std"] = metrics.f1.std
+    row[f"{prefix}_auc_mean"] = metrics.auc.mean
+    row[f"{prefix}_auc_std"] = metrics.auc.std
diff --git a/pelinker/reports/summary.py b/pelinker/reports/summary.py
new file mode 100644
index 0000000..e919c0a
--- /dev/null
+++ b/pelinker/reports/summary.py
@@ -0,0 +1,241 @@
+"""Flat-row conversion and cross-report aggregation for search summaries."""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Sequence
+
+import numpy as np
+
+
+from pelinker.reports.schema import (
+    AllScreenerCvResult,
+    BinaryClassifierMetrics,
+    ClusteringSearchSummaryRow,
+    HyperparameterSearchStats,
+    MeanWithUncertainty,
+    MetricMeanStd,
+    ModelSelectionReport,
+    _pool_all_screener_cv_results,
+)
+
+
+def _binary_metrics_from_flat_row(
+    row: dict[str, str | float | None],
+    prefix: str,
+) -> BinaryClassifierMetrics:
+    """Inverse of :func:`_binary_metrics_into_row` / JSON branch of :func:`_binary_metrics_to_jsonable`."""
+
+    def _m(field: str) -> MetricMeanStd:
+        mk = f"{prefix}_{field}_mean"
+        sk = f"{prefix}_{field}_std"
+        return MetricMeanStd(
+            mean=float(row[mk]),
+            std=float(row.get(sk) or 0.0),
+        )
+
+    return BinaryClassifierMetrics(
+        precision=_m("precision"),
+        recall=_m("recall"),
+        f1=_m("f1"),
+        auc=_m("auc"),
+    )
+
+
+def _all_screener_cv_from_flat_row(
+    row: dict[str, str | float | None],
+) -> AllScreenerCvResult | None:
+    """Reconstruct unified CV summary from flat CSV/checkpoint rows."""
+    if "screener_lda_precision_mean" not in row or "screener_precision_mean" not in row:
+        return None
+    sb = row.get("screener_best_kind")
+    ow = row.get("oov_winner_kind")
+    if (
+        not isinstance(sb, str)
+        or not sb
+        or not isinstance(ow, str)
+        or not ow
+        or "combined_precision_mean" not in row
+    ):
+        return None
+    return AllScreenerCvResult(
+        screener_lda=_binary_metrics_from_flat_row(row, "screener_lda"),
+        screener_svm=_binary_metrics_from_flat_row(row, "screener_svm"),
+        screener_best_kind=sb,
+        screener_best=_binary_metrics_from_flat_row(row, "screener"),
+        oov_winner_kind=ow,
+        oov=_binary_metrics_from_flat_row(row, "oov"),
+        combined=_binary_metrics_from_flat_row(row, "combined"),
+    )
+
+
+def clustering_search_summary_row_from_flat_dict(
+    row: dict[str, str | float | None],
+) -> ClusteringSearchSummaryRow:
+    """Reconstruct :class:`ClusteringSearchSummaryRow` from :meth:`to_flat_dict` output."""
+    ari_raw = row.get("ari")
+    ari_block: MeanWithUncertainty | None
+    if ari_raw is None or (isinstance(ari_raw, float) and math.isnan(ari_raw)):
+        ari_block = None
+    else:
+        ari_block = MeanWithUncertainty(
+            mean=float(ari_raw),
+            std=float(row.get("ari_std") or 0.0),
+        )
+    # Absent in checkpoints written before n_rows_realized existed; stays None there.
+    nrr_raw = row.get("n_rows_realized")
+    nrr_block: MeanWithUncertainty | None
+    if nrr_raw is None or (isinstance(nrr_raw, float) and math.isnan(nrr_raw)):
+        nrr_block = None
+    else:
+        nrr_block = MeanWithUncertainty(
+            mean=float(nrr_raw),
+            std=float(row.get("n_rows_realized_std") or 0.0),
+        )
+    return ClusteringSearchSummaryRow(
+        model=str(row["model"]),
+        layer=str(row["layer"]),
+        hyperparameters=HyperparameterSearchStats(
+            min_cluster_size=MeanWithUncertainty(
+                mean=float(row["best_size"]),
+                std=float(row["best_size_std"] or 0.0),
+            ),
+        ),
+        number_properties=MeanWithUncertainty(
+            mean=float(row["number_properties"]),
+            std=float(row["number_properties_std"] or 0.0),
+        ),
+        n_clusters_emergent=MeanWithUncertainty(
+            mean=float(row["n_clusters_emergent"]),
+            std=float(row["n_clusters_emergent_std"] or 0.0),
+        ),
+        dbcv=MeanWithUncertainty(
+            mean=float(row["best_score"]),
+            std=float(row["best_score_std"] or 0.0),
+        ),
+        ari=ari_block,
+        all_screener_cv=_all_screener_cv_from_flat_row(row),
+        n_rows_realized=nrr_block,
+    )
+
+
+def summarize_clustering_reports_for_search(
+    reports: Sequence[ModelSelectionReport],
+    *,
+    model: str,
+    layer: str,
+    pooled_min_cluster_size: int | None = None,
+) -> ClusteringSearchSummaryRow:
+    """
+    Aggregate repeated :class:`ClusteringReport` runs into one search summary row.
+
+    When ``pooled_min_cluster_size`` is set (after aggregating grid curves across samples),
+    ``best_size`` / ``best_size_std`` report that single consensus hyperparameter (std is 0)
+    and ``dbcv`` is the mean (and std) of each sample's DBCV **at that grid point**.
+    ``n_clusters_emergent`` remains the mean of per-sample fits at each sample's grid-optimal
+    size; use :func:`n_clusters_at_min_cluster_size` on pooled grid CSV rows for a fixed MCS.
+
+    Otherwise (independent runs or legacy callers) ``best_size`` is the mean of per-report
+    chosen sizes and ``dbcv`` is the mean of each report's ``best_score``.
+
+    Raises:
+        ValueError: if ``reports`` is empty.
+    """
+    if not reports:
+        raise ValueError("reports must be non-empty")
+
+    sizes = np.array(
+        [r.hyperparameters.min_cluster_size for r in reports], dtype=np.float64
+    )
+    scores = np.array([r.best_score for r in reports], dtype=np.float64)
+    nprops = np.array([r.number_properties for r in reports], dtype=np.float64)
+    n_clusters = np.array([r.n_clusters_emergent for r in reports], dtype=np.float64)
+    ari_vals = [float(r.ari) for r in reports if r.ari is not None]
+
+    n = len(reports)
+    std_nprops = float(np.std(nprops)) if n > 1 else 0.0
+    std_n_clusters = float(np.std(n_clusters)) if n > 1 else 0.0
+
+    def _metric_at_pooled(column: str) -> list[float]:
+        """Per-sample value of ``column`` at the pooled MCS (skipping samples without it)."""
+        out: list[float] = []
+        for r in reports:
+            m = r.metrics_df
+            if column not in m.columns:
+                continue
+            hit = m.loc[m["min_cluster_size"] == pooled_min_cluster_size, column]
+            if len(hit) > 0 and np.isfinite(float(hit.iloc[0])):
+                out.append(float(hit.iloc[0]))
+        return out
+
+    if pooled_min_cluster_size is not None:
+        sizes_mean = float(pooled_min_cluster_size)
+        std_sizes = 0.0
+        dbcv_at = _metric_at_pooled("dbcv")
+        if dbcv_at:
+            arr_dbcv = np.array(dbcv_at, dtype=np.float64)
+            dbcv_mean = float(np.mean(arr_dbcv))
+            dbcv_std = float(np.std(arr_dbcv)) if len(arr_dbcv) > 1 else 0.0
+        else:
+            dbcv_mean = float(np.mean(scores))
+            dbcv_std = float(np.std(scores)) if n > 1 else 0.0
+        # DBCV and ARI must be read at the *same* min_cluster_size: they are combined into
+        # one outer score, and r.ari is measured at each sample's own best size, not here.
+        ari_at_pooled = _metric_at_pooled("ari")
+        if ari_at_pooled:
+            ari_vals = ari_at_pooled
+    else:
+        sizes_mean = float(np.mean(sizes))
+        std_sizes = float(np.std(sizes)) if n > 1 else 0.0
+        dbcv_mean = float(np.mean(scores))
+        dbcv_std = float(np.std(scores)) if n > 1 else 0.0
+
+    ari_block: MeanWithUncertainty | None
+    if ari_vals:
+        arr = np.array(ari_vals, dtype=np.float64)
+        ari_block = MeanWithUncertainty(
+            mean=float(np.mean(arr)),
+            std=float(np.std(arr)) if len(arr) > 1 else 0.0,
+        )
+    else:
+        ari_block = None
+
+    acv_reports = [r.all_screener_cv for r in reports if r.all_screener_cv is not None]
+    pooled_acv = _pool_all_screener_cv_results(acv_reports) if acv_reports else None
+
+    rows_realized = [
+        float(r.n_rows_realized) for r in reports if r.n_rows_realized is not None
+    ]
+    n_rows_block: MeanWithUncertainty | None = None
+    if rows_realized:
+        arr_rows = np.array(rows_realized, dtype=np.float64)
+        n_rows_block = MeanWithUncertainty(
+            mean=float(np.mean(arr_rows)),
+            std=float(np.std(arr_rows)) if len(arr_rows) > 1 else 0.0,
+        )
+
+    return ClusteringSearchSummaryRow(
+        model=model,
+        layer=layer,
+        hyperparameters=HyperparameterSearchStats(
+            min_cluster_size=MeanWithUncertainty(
+                mean=sizes_mean,
+                std=std_sizes,
+            ),
+        ),
+        number_properties=MeanWithUncertainty(
+            mean=float(np.mean(nprops)),
+            std=std_nprops,
+        ),
+        n_clusters_emergent=MeanWithUncertainty(
+            mean=float(np.mean(n_clusters)),
+            std=std_n_clusters,
+        ),
+        dbcv=MeanWithUncertainty(
+            mean=dbcv_mean,
+            std=dbcv_std,
+        ),
+        ari=ari_block,
+        all_screener_cv=pooled_acv,
+        n_rows_realized=n_rows_block,
+    )
diff --git a/pelinker/screener/ambient_screener.py b/pelinker/screener/ambient.py
similarity index 99%
rename from pelinker/screener/ambient_screener.py
rename to pelinker/screener/ambient.py
index 6ae2687..a2e6211 100644
--- a/pelinker/screener/ambient_screener.py
+++ b/pelinker/screener/ambient.py
@@ -11,7 +11,7 @@
 from sklearn.model_selection import StratifiedShuffleSplit
 from sklearn.svm import LinearSVC
 
-from pelinker.config import NegativeScreenerConfig, ScreenerKind
+from pelinker.core.config import NegativeScreenerConfig, ScreenerKind
 
 
 def _linear_svc_for_embeddings(X: np.ndarray, *, random_state: int | None) -> LinearSVC:
diff --git a/pelinker/screener/evaluation.py b/pelinker/screener/evaluation.py
new file mode 100644
index 0000000..1570afb
--- /dev/null
+++ b/pelinker/screener/evaluation.py
@@ -0,0 +1,569 @@
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import pandas as pd
+from pelinker.reports.schema import (
+    AllScreenerCvResult,
+    BinaryClassifierMetrics,
+    MetricMeanStd,
+    NegativeScreenerInSampleMetrics,
+    PerDatapointScores,
+)
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.metrics import (
+    f1_score,
+    precision_score,
+    recall_score,
+    roc_auc_score,
+)
+from sklearn.model_selection import StratifiedKFold
+
+from pelinker.core.config import (
+    ManifoldOovScreenerConfig,
+    NegativeScreenerConfig,
+)
+from pelinker.screener.projection import (
+    ManifoldOovKind,
+    make_manifold_linear_svc,
+    make_manifold_rbf_svc,
+    oov_estimator_scores,
+    pick_projection_winner_by_mean_f1,
+)
+from pelinker.screener.ambient import (
+    NegativeClassScreener,
+    _linear_svc_for_embeddings,
+)
+
+"""Cross-validated evaluation of the negative and manifold-OOV screeners."""
+
+
+def split_by_negative_label(
+    dfr: pd.DataFrame,
+    negative_label: str,
+) -> tuple[np.ndarray, pd.DataFrame]:
+    """
+    Split a mention frame into a boolean mask of synthetic-negative rows and the
+    manifold frame (KB / non-negative rows only).
+    """
+    neg_mask = dfr["entity"].astype(str).values == negative_label
+    manifold_df = dfr.loc[~neg_mask].copy()
+    return neg_mask, manifold_df
+
+
+def _pick_oov_winner_by_cv_folds(
+    fold_pairs: list[tuple[np.ndarray, np.ndarray]],
+    Xm: np.ndarray,
+    y_i: np.ndarray,
+    oov_cfg: ManifoldOovScreenerConfig,
+) -> tuple[bool, ManifoldOovKind | str]:
+    svm_oov_fold: list[float] = []
+    lda_oov_fold: list[float] = []
+    rbf_oov_fold: list[float] = []
+
+    for train_idx, test_idx in fold_pairs:
+        Xtr_m, Xtst_m = Xm[train_idx], Xm[test_idx]
+        ytr_m, ytst_m = y_i[train_idx], y_i[test_idx]
+        if len(np.unique(ytst_m)) < 2:
+            continue
+        xm64 = Xtr_m.astype(np.float64, copy=False)
+        xt64 = Xtst_m.astype(np.float64, copy=False)
+
+        lin_o = make_manifold_linear_svc(xm64, oov_cfg)
+        lin_o.fit(xm64, ytr_m)
+        svm_oov_fold.append(
+            float(
+                f1_score(
+                    ytst_m,
+                    lin_o.predict(xt64),
+                    pos_label=1,
+                    zero_division=0,
+                )
+            )
+        )
+
+        rbf_o = make_manifold_rbf_svc(oov_cfg)
+        rbf_o.fit(xm64, ytr_m)
+        rbf_oov_fold.append(
+            float(
+                f1_score(
+                    ytst_m,
+                    rbf_o.predict(xt64),
+                    pos_label=1,
+                    zero_division=0,
+                )
+            )
+        )
+
+        lda_est_o = LinearDiscriminantAnalysis(solver="svd")
+        lda_est_o.fit(xm64, ytr_m)
+        lda_oov_fold.append(
+            float(
+                f1_score(
+                    ytst_m,
+                    lda_est_o.predict(xt64),
+                    pos_label=1,
+                    zero_division=0,
+                )
+            )
+        )
+
+    if not lda_oov_fold:
+        return False, "lda"
+    svm_m_o = float(np.mean(svm_oov_fold))
+    lda_m_o = float(np.mean(lda_oov_fold))
+    rbf_m_o = float(np.mean(rbf_oov_fold))
+    return True, pick_projection_winner_by_mean_f1(lda_m_o, svm_m_o, rbf_m_o)
+
+
+def _collect_embedding_cv_folds(
+    fold_pairs: list[tuple[np.ndarray, np.ndarray]],
+    Xe: np.ndarray,
+    y_i: np.ndarray,
+    rs_emb: int,
+) -> (
+    tuple[
+        list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
+        str,
+    ]
+    | None
+):
+    embed_fold_rows: list[
+        tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
+    ] = []
+    pool_y_pooled: list[int] = []
+    pool_lda_pooled: list[float] = []
+    pool_svm_pooled: list[float] = []
+    for train_idx, test_idx in fold_pairs:
+        Xtr_e, Xtst_e = Xe[train_idx], Xe[test_idx]
+        ytr_e, ytst_e = y_i[train_idx], y_i[test_idx]
+        if len(np.unique(ytst_e)) < 2:
+            continue
+
+        lda_emb = LinearDiscriminantAnalysis(solver="svd")
+        lda_emb.fit(Xtr_e, ytr_e)
+        sc_lda = np.asarray(lda_emb.transform(Xtst_e), dtype=np.float64).ravel()
+        svm_emb = _linear_svc_for_embeddings(Xtr_e, random_state=rs_emb)
+        svm_emb.fit(Xtr_e, ytr_e)
+        sc_svm = np.asarray(
+            svm_emb.decision_function(Xtst_e),
+            dtype=np.float64,
+        ).ravel()
+        embed_fold_rows.append((train_idx, test_idx, ytst_e, sc_lda, sc_svm))
+        for j in range(int(ytst_e.shape[0])):
+            pool_y_pooled.append(int(ytst_e[j]))
+            pool_lda_pooled.append(float(sc_lda[j]))
+            pool_svm_pooled.append(float(sc_svm[j]))
+
+    if not pool_y_pooled:
+        return None
+
+    y_p = np.asarray(pool_y_pooled, dtype=np.int64)
+    s_ld_p = np.asarray(pool_lda_pooled, dtype=np.float64)
+    s_sv_p = np.asarray(pool_svm_pooled, dtype=np.float64)
+    auc_lda_g = float(roc_auc_score(y_p, s_ld_p))
+    auc_svm_g = float(roc_auc_score(y_p, s_sv_p))
+    screener_best_kind = "lda" if auc_lda_g >= auc_svm_g else "svm"
+    return embed_fold_rows, screener_best_kind
+
+
+def _oov_scores_for_fold(
+    winner_kind_oov: ManifoldOovKind | str,
+    Xm: np.ndarray,
+    train_idx: np.ndarray,
+    test_idx: np.ndarray,
+    y_i: np.ndarray,
+    oov_cfg: ManifoldOovScreenerConfig,
+) -> np.ndarray:
+    Xtr_m, Xtst_m = Xm[train_idx], Xm[test_idx]
+    ytr_e = y_i[train_idx]
+    xm_tr = Xtr_m.astype(np.float64, copy=False)
+    xm_ts = Xtst_m.astype(np.float64, copy=False)
+    if winner_kind_oov == "svm":
+        svm_w = make_manifold_linear_svc(xm_tr, oov_cfg)
+        svm_w.fit(xm_tr, ytr_e)
+        return oov_estimator_scores(svm_w, xm_ts)
+    if winner_kind_oov == "rbf":
+        rbf_w = make_manifold_rbf_svc(oov_cfg)
+        rbf_w.fit(xm_tr, ytr_e)
+        return oov_estimator_scores(rbf_w, xm_ts)
+    lda_w = LinearDiscriminantAnalysis(solver="svd")
+    lda_w.fit(xm_tr, ytr_e)
+    return oov_estimator_scores(lda_w, xm_ts)
+
+
+def _score_folds_with_oov_and_pool(
+    embed_fold_rows: list[
+        tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
+    ],
+    *,
+    screener_best_kind: str,
+    oov_disabled: bool,
+    winner_kind_oov: ManifoldOovKind | str,
+    Xm: np.ndarray | None,
+    y_i: np.ndarray,
+    entity: np.ndarray,
+    orig_idx: np.ndarray,
+    oov_cfg: ManifoldOovScreenerConfig,
+) -> (
+    tuple[
+        list[int],
+        list[str],
+        list[int],
+        list[float],
+        list[float],
+        list[float],
+        list[float],
+        list[float],
+        list[list[float]],
+    ]
+    | None
+):
+    pool_orig: list[int] = []
+    pool_ent: list[str] = []
+    pool_y: list[int] = []
+    pool_lda_s: list[float] = []
+    pool_svm_s: list[float] = []
+    pool_sb: list[float] = []
+    pool_oov: list[float] = []
+    pool_comb: list[float] = []
+
+    lda_p, lda_r, lda_f1, lda_auc_l = [], [], [], []
+    svm_p, svm_r, svm_f1, svm_auc_l = [], [], [], []
+    sb_p, sb_r, sb_f1, sb_a = [], [], [], []
+    oov_p, oov_r, oov_f1, oov_a = [], [], [], []
+    cb_p, cb_r, cb_f1, cb_a = [], [], [], []
+
+    for train_idx, test_idx, ytst_e, sc_lda, sc_svm in embed_fold_rows:
+        sb_scores = sc_lda if screener_best_kind == "lda" else sc_svm
+
+        if not oov_disabled and Xm is not None:
+            o_scores = _oov_scores_for_fold(
+                winner_kind_oov, Xm, train_idx, test_idx, y_i, oov_cfg
+            )
+            mn_s = _minmax01_fold(sb_scores)
+            mn_o = _minmax01_fold(o_scores)
+            comb_scores = 0.5 * mn_s + 0.5 * mn_o
+        else:
+            o_scores = np.zeros_like(sb_scores, dtype=np.float64)
+            comb_scores = sb_scores.astype(np.float64, copy=False)
+
+        oi_fold = np.asarray(orig_idx[test_idx], dtype=np.int64).ravel()
+        ent_fold = entity[test_idx]
+        yt = ytst_e.astype(np.int64, copy=False)
+
+        for j in range(int(yt.shape[0])):
+            pool_orig.append(int(oi_fold[j]))
+            pool_ent.append(str(ent_fold[j]))
+            pool_y.append(int(yt[j]))
+            pool_lda_s.append(float(sc_lda[j]))
+            pool_svm_s.append(float(sc_svm[j]))
+            pool_sb.append(float(sb_scores[j]))
+            pool_oov.append(float(o_scores[j]))
+            pool_comb.append(float(comb_scores[j]))
+
+        p0, r0, f00, a0 = _fold_prfa(ytst_e, sc_lda)
+        lda_p.append(p0)
+        lda_r.append(r0)
+        lda_f1.append(f00)
+        lda_auc_l.append(a0)
+        p1, r1, f01, a1 = _fold_prfa(ytst_e, sc_svm)
+        svm_p.append(p1)
+        svm_r.append(r1)
+        svm_f1.append(f01)
+        svm_auc_l.append(a1)
+        p2, r2, f02, a2 = _fold_prfa(ytst_e, sb_scores)
+        sb_p.append(p2)
+        sb_r.append(r2)
+        sb_f1.append(f02)
+        sb_a.append(a2)
+        p3, r3, f03, a3 = _fold_prfa(ytst_e, o_scores)
+        oov_p.append(p3)
+        oov_r.append(r3)
+        oov_f1.append(f03)
+        oov_a.append(a3)
+        p4, r4, f04, a4 = _fold_prfa(ytst_e, comb_scores)
+        cb_p.append(p4)
+        cb_r.append(r4)
+        cb_f1.append(f04)
+        cb_a.append(a4)
+
+    if not lda_p:
+        return None
+
+    fold_lists = [
+        lda_p,
+        lda_r,
+        lda_f1,
+        lda_auc_l,
+        svm_p,
+        svm_r,
+        svm_f1,
+        svm_auc_l,
+        sb_p,
+        sb_r,
+        sb_f1,
+        sb_a,
+        oov_p,
+        oov_r,
+        oov_f1,
+        oov_a,
+        cb_p,
+        cb_r,
+        cb_f1,
+        cb_a,
+    ]
+    return (
+        pool_orig,
+        pool_ent,
+        pool_y,
+        pool_lda_s,
+        pool_svm_s,
+        pool_sb,
+        pool_oov,
+        pool_comb,
+        fold_lists,
+    )
+
+
+def _build_all_screener_cv_result(
+    fold_lists: list[list[float]],
+    *,
+    screener_best_kind: str,
+    oov_kind_disp: str,
+    oov_disabled: bool,
+) -> AllScreenerCvResult:
+    (
+        lda_p,
+        lda_r,
+        lda_f1,
+        lda_auc_l,
+        svm_p,
+        svm_r,
+        svm_f1,
+        svm_auc_l,
+        sb_p,
+        sb_r,
+        sb_f1,
+        sb_a,
+        oov_p,
+        oov_r,
+        oov_f1,
+        oov_a,
+        cb_p,
+        cb_r,
+        cb_f1,
+        cb_a,
+    ) = fold_lists
+    lda_mets = _metrics_from_fold_lists(lda_p, lda_r, lda_f1, lda_auc_l)
+    svm_mets = _metrics_from_fold_lists(svm_p, svm_r, svm_f1, svm_auc_l)
+    sb_mets = _metrics_from_fold_lists(sb_p, sb_r, sb_f1, sb_a)
+    if oov_disabled:
+        oov_mets = _zero_binary_metrics()
+        comb_mets = sb_mets
+    else:
+        oov_mets = _metrics_from_fold_lists(oov_p, oov_r, oov_f1, oov_a)
+        comb_mets = _metrics_from_fold_lists(cb_p, cb_r, cb_f1, cb_a)
+    return AllScreenerCvResult(
+        screener_lda=lda_mets,
+        screener_svm=svm_mets,
+        screener_best_kind=screener_best_kind,
+        screener_best=sb_mets,
+        oov_winner_kind=oov_kind_disp,
+        oov=oov_mets,
+        combined=comb_mets,
+    )
+
+
+def _unified_cv_fold_count(y: np.ndarray, n_splits_requested: int) -> int | None:
+    n0 = int(np.sum(y == 0))
+    n1 = int(np.sum(y == 1))
+    if n0 < 2 or n1 < 2:
+        return None
+    max_splits = min(n0, n1)
+    n_eff = min(int(n_splits_requested), max_splits)
+    return n_eff if n_eff >= 2 else None
+
+
+def _minmax01_fold(x: np.ndarray) -> np.ndarray:
+    xf = np.asarray(x, dtype=np.float64).ravel()
+    lo = float(np.min(xf))
+    hi = float(np.max(xf))
+    if hi <= lo:
+        return np.full(xf.shape[0], 0.5, dtype=np.float64)
+    return (xf - lo) / (hi - lo)
+
+
+def _fold_prfa(
+    y_true: np.ndarray, scores: np.ndarray
+) -> tuple[float, float, float, float]:
+    y_i = np.asarray(y_true, dtype=np.int64).ravel()
+    s = np.asarray(scores, dtype=np.float64).ravel()
+    y_pred = (s > 0.0).astype(np.int64)
+    prec = float(precision_score(y_i, y_pred, pos_label=1, zero_division=0))
+    rec = float(recall_score(y_i, y_pred, pos_label=1, zero_division=0))
+    f1 = float(f1_score(y_i, y_pred, pos_label=1, zero_division=0))
+    try:
+        auc_v = float(roc_auc_score(y_i, s))
+    except ValueError:
+        auc_v = 0.5
+    if math.isnan(auc_v) or math.isinf(auc_v):
+        auc_v = 0.5
+    return prec, rec, f1, auc_v
+
+
+def _metrics_from_fold_lists(
+    precs: list[float],
+    recalls: list[float],
+    f1s: list[float],
+    aucs: list[float],
+) -> BinaryClassifierMetrics:
+    def _one(vals: list[float]) -> MetricMeanStd:
+        arr = np.asarray(vals, dtype=np.float64)
+        return MetricMeanStd(
+            mean=float(np.mean(arr)),
+            std=float(np.std(arr, ddof=1)) if arr.size > 1 else 0.0,
+        )
+
+    return BinaryClassifierMetrics(
+        precision=_one(precs),
+        recall=_one(recalls),
+        f1=_one(f1s),
+        auc=_one(aucs),
+    )
+
+
+def _zero_binary_metrics() -> BinaryClassifierMetrics:
+    z = MetricMeanStd(0.0, 0.0)
+    return BinaryClassifierMetrics(
+        precision=z, recall=z, f1=z, auc=MetricMeanStd(0.5, 0.0)
+    )
+
+
+def evaluate_all_screeners_cv(
+    X_embed: np.ndarray,
+    X_manifold: np.ndarray | None,
+    y: np.ndarray,
+    entity: np.ndarray,
+    orig_idx: np.ndarray,
+    screener_cfg: NegativeScreenerConfig,
+    oov_cfg: ManifoldOovScreenerConfig,
+) -> tuple[AllScreenerCvResult, PerDatapointScores] | None:
+    """
+    Shared-stratified-fold CV for LDA/SVM negative screener, manifold OOV model, and stacked score.
+
+    ``screener_best`` scores use the ROC winner (LDA vs SVM) on pooled OOS predictions.
+
+    When ``oov_cfg.enabled`` is False or ``X_manifold`` is None, OOV branch is skipped:
+    ``combined`` metrics match ``screener_best`` and ``oov_winner_kind`` is ``"disabled"``.
+    """
+    y_i = np.asarray(y, dtype=np.int64).ravel()
+    n_splits = _unified_cv_fold_count(y_i, screener_cfg.cv_n_splits)
+    if n_splits is None:
+        return None
+
+    splitter = StratifiedKFold(
+        n_splits=n_splits,
+        shuffle=True,
+        random_state=screener_cfg.cv_random_state,
+    )
+    Xe = np.asarray(X_embed, dtype=np.float64)
+    fold_pairs = list(splitter.split(Xe, y_i))
+    rs_emb = screener_cfg.cv_random_state
+
+    oov_run = bool(oov_cfg.enabled) and X_manifold is not None
+    Xm: np.ndarray | None = (
+        np.asarray(X_manifold, dtype=np.float64) if oov_run else None
+    )
+    winner_kind_oov: ManifoldOovKind | str = "lda"
+
+    if oov_run and Xm is not None:
+        oov_run, winner_kind_oov = _pick_oov_winner_by_cv_folds(
+            fold_pairs, Xm, y_i, oov_cfg
+        )
+        if not oov_run:
+            Xm = None
+
+    collected = _collect_embedding_cv_folds(fold_pairs, Xe, y_i, rs_emb)
+    if collected is None:
+        return None
+    embed_fold_rows, screener_best_kind = collected
+
+    oov_disabled = not oov_run or Xm is None
+    oov_kind_disp = "disabled" if oov_disabled else str(winner_kind_oov)
+
+    scored = _score_folds_with_oov_and_pool(
+        embed_fold_rows,
+        screener_best_kind=screener_best_kind,
+        oov_disabled=oov_disabled,
+        winner_kind_oov=winner_kind_oov,
+        Xm=Xm,
+        y_i=y_i,
+        entity=entity,
+        orig_idx=orig_idx,
+        oov_cfg=oov_cfg,
+    )
+    if scored is None:
+        return None
+    (
+        pool_orig,
+        pool_ent,
+        pool_y,
+        pool_lda_s,
+        pool_svm_s,
+        pool_sb,
+        pool_oov,
+        pool_comb,
+        fold_lists,
+    ) = scored
+
+    result = _build_all_screener_cv_result(
+        fold_lists,
+        screener_best_kind=screener_best_kind,
+        oov_kind_disp=oov_kind_disp,
+        oov_disabled=oov_disabled,
+    )
+
+    datapoints = PerDatapointScores(
+        orig_idx=list(pool_orig),
+        entity=list(pool_ent),
+        y_true=list(pool_y),
+        screener_lda_score=list(pool_lda_s),
+        screener_svm_score=list(pool_svm_s),
+        screener_best_score=list(pool_sb),
+        oov_score=list(pool_oov),
+        combined_score=list(pool_comb),
+    )
+    return result, datapoints
+
+
+def fit_ambient_screener_with_metrics(
+    dfr: pd.DataFrame,
+    config: NegativeScreenerConfig,
+) -> tuple[NegativeClassScreener, NegativeScreenerInSampleMetrics | None]:
+    """
+    Fit the persisted screener on ``dfr`` and report in-sample PR/F1 for detecting
+    ``negative_label`` when both classes are present.
+    """
+    screener = NegativeClassScreener.fit_from_frame(dfr, config)
+    y_true = (dfr["entity"].astype(str).values == config.negative_label).astype(
+        np.int64
+    )
+    n_kb = int(np.sum(y_true == 0))
+    n_neg = int(np.sum(y_true == 1))
+    if n_kb == 0 or n_neg == 0:
+        return screener, None
+    X = np.stack(dfr["embed"].values).astype(np.float32, copy=False)
+    y_pred = screener.predict_is_negative(X).astype(np.int64)
+    prec = float(precision_score(y_true, y_pred, pos_label=1, zero_division=0))
+    rec = float(recall_score(y_true, y_pred, pos_label=1, zero_division=0))
+    f1 = float(f1_score(y_true, y_pred, pos_label=1, zero_division=0))
+    return screener, NegativeScreenerInSampleMetrics(
+        precision=prec,
+        recall=rec,
+        f1=f1,
+        n_kb_mentions=n_kb,
+        n_negative_label_mentions=n_neg,
+        kind=config.kind,
+    )
diff --git a/pelinker/screener/projection_screener.py b/pelinker/screener/projection.py
similarity index 99%
rename from pelinker/screener/projection_screener.py
rename to pelinker/screener/projection.py
index 19839a2..01cc9eb 100644
--- a/pelinker/screener/projection_screener.py
+++ b/pelinker/screener/projection.py
@@ -12,7 +12,7 @@
 from sklearn.model_selection import StratifiedKFold
 from sklearn.svm import LinearSVC, SVC
 
-from pelinker.config import ManifoldOovScreenerConfig
+from pelinker.core.config import ManifoldOovScreenerConfig
 
 ManifoldOovKind = Literal["lda", "svm", "rbf"]
 
diff --git a/pelinker/search/__init__.py b/pelinker/search/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pelinker/checkpoint.py b/pelinker/search/checkpoint_base.py
similarity index 78%
rename from pelinker/checkpoint.py
rename to pelinker/search/checkpoint_base.py
index 67831e1..95b8b4b 100644
--- a/pelinker/checkpoint.py
+++ b/pelinker/search/checkpoint_base.py
@@ -18,10 +18,14 @@
 from hashlib import sha256
 from typing import Any
 
-from pelinker.io.json_files import is_gzip_file_path, load_json_path
-from pelinker.onto import NEGATIVE_LABEL
+from pelinker.data.json_files import is_gzip_file_path, load_json_path
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.kb.classes import ClassView, file_sha256
 
-CHECKPOINT_VERSION = 1
+CHECKPOINT_VERSION = 2
+"""Bumped to 2: outer DBCV+ARI scores switched from ``0.5*(dbcv+ari)`` to the clipped
+geometric mean. ``singleton_scores_by_key`` persists those values, so resuming a v1
+checkpoint would compare old-scale and new-scale scores against each other."""
 
 
 @dataclass
@@ -93,11 +97,13 @@ def shared_fingerprint_fields(
     max_mentions_per_entity: int | None = None,
     max_mentions_negative: int | None = None,
     mention_cap_seed: int = 13,
+    class_view: ClassView = "raw",
+    class_kb_path: pathlib.Path | None = None,
 ) -> dict[str, Any]:
     """The fingerprint fields common to every search; callers add their own on top.
 
     ``min_scale`` is resolved here to the same default
-    :meth:`~pelinker.config.ClusteringOptimizationConfig.resolved_min_scale` uses, so
+    :meth:`~pelinker.core.config.ClusteringOptimizationConfig.resolved_min_scale` uses, so
     an explicit ``min_scale`` equal to the default does not change the fingerprint.
     """
     kb = None
@@ -106,7 +112,18 @@ def shared_fingerprint_fields(
     resolved_min_scale = (
         min_scale if min_scale is not None else max(1, min_class_size // 2)
     )
+    # A class view changes what the objective's ARI scores against, so it is part of the
+    # fingerprint. It is added only when set, so a raw-label run keeps the fingerprint it
+    # had before the option existed. The KB enters by content: a re-derived pairs KB
+    # changes the classes under the same path.
+    view_fields: dict[str, Any] = {}
+    if class_view != "raw":
+        view_fields["class_view"] = class_view
+        view_fields["class_kb_sha256"] = (
+            file_sha256(class_kb_path) if class_kb_path is not None else None
+        )
     return {
+        **view_fields,
         "batch_size": batch_size,
         "clustering_grid_step": clustering_grid_step,
         "clustering_sample_rows": clustering_sample_rows,
diff --git a/pelinker/dim_selection/__init__.py b/pelinker/search/dim_selection/__init__.py
similarity index 63%
rename from pelinker/dim_selection/__init__.py
rename to pelinker/search/dim_selection/__init__.py
index 3f4b06b..1df5b83 100644
--- a/pelinker/dim_selection/__init__.py
+++ b/pelinker/search/dim_selection/__init__.py
@@ -1,5 +1,5 @@
 """PCA / UMAP dimension selection (grid search on one embedding combination)."""
 
-from pelinker.dim_selection.runner import run_dim_selection
+from pelinker.search.dim_selection.runner import run_dim_selection
 
 __all__ = ["run_dim_selection"]
diff --git a/pelinker/dim_selection/checkpoint.py b/pelinker/search/dim_selection/checkpoint.py
similarity index 93%
rename from pelinker/dim_selection/checkpoint.py
rename to pelinker/search/dim_selection/checkpoint.py
index 239f3fa..e9b09fd 100644
--- a/pelinker/dim_selection/checkpoint.py
+++ b/pelinker/search/dim_selection/checkpoint.py
@@ -1,7 +1,7 @@
-"""Structured checkpoint I/O for ``pelinker.dim_selection`` runs.
+"""Structured checkpoint I/O for ``pelinker.search.dim_selection`` runs.
 
 Shared machinery (version, fingerprint, atomic gzip-aware I/O, the fingerprint fields
-common to every search) lives in :mod:`pelinker.checkpoint`; only the cell-keyed
+common to every search) lives in :mod:`pelinker.search.checkpoint_base`; only the cell-keyed
 schema is here.
 """
 
@@ -11,7 +11,7 @@
 from dataclasses import dataclass, field
 from typing import Any
 
-from pelinker.checkpoint import (
+from pelinker.search.checkpoint_base import (
     CHECKPOINT_VERSION,
     compute_run_fingerprint,
     read_checkpoint_json,
@@ -20,7 +20,8 @@
     utc_now_iso,
     write_checkpoint_json_atomic,
 )
-from pelinker.onto import NEGATIVE_LABEL
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.kb.classes import ClassView
 
 __all__ = [
     "CHECKPOINT_VERSION",
@@ -132,6 +133,8 @@ def fingerprint_config_from_cli(
     max_mentions_per_entity: int | None = None,
     max_mentions_negative: int | None = None,
     mention_cap_seed: int = 13,
+    class_view: ClassView = "raw",
+    class_kb_path: pathlib.Path | None = None,
 ) -> dict[str, Any]:
     return {
         **shared_fingerprint_fields(
@@ -154,6 +157,8 @@ def fingerprint_config_from_cli(
             max_mentions_per_entity=max_mentions_per_entity,
             max_mentions_negative=max_mentions_negative,
             mention_cap_seed=mention_cap_seed,
+            class_view=class_view,
+            class_kb_path=class_kb_path,
         ),
         "input_parquet": str(input_parquet.expanduser().resolve()),
         "layer": layer,
diff --git a/pelinker/dim_selection/grids.py b/pelinker/search/dim_selection/grids.py
similarity index 95%
rename from pelinker/dim_selection/grids.py
rename to pelinker/search/dim_selection/grids.py
index 1d2b416..65c4ecb 100644
--- a/pelinker/dim_selection/grids.py
+++ b/pelinker/search/dim_selection/grids.py
@@ -121,10 +121,10 @@ def pick_winner_row(df_results: pd.DataFrame) -> dict[str, object]:
     Choose the best (pca, umap) row by outer DBCV+ARI score.
 
     ``outer_score`` = min–max pooled mean DBCV + mean ARI across candidate cells
-    (same pooling as inner ``dbcv_ari_mean_minmax``). Ties: lower outer std, then
+    (same formula as inner ``dbcv_ari_geomean``). Ties: lower outer std, then
     smaller ``pca_components``, then smaller ``umap_dim``.
     """
-    from pelinker.clustering_search_ranking import pick_best_row
+    from pelinker.clustering.ranking import pick_best_row
 
     if df_results.empty:
         raise ValueError("df_results must be a non-empty DataFrame")
@@ -141,5 +141,4 @@ def pick_winner_row(df_results: pd.DataFrame) -> dict[str, object]:
     return pick_best_row(
         df_results,
         tie_break_cols=("pca_components", "umap_dim"),
-        use_minmax=True,
     )
diff --git a/pelinker/dim_selection/runner.py b/pelinker/search/dim_selection/runner.py
similarity index 88%
rename from pelinker/dim_selection/runner.py
rename to pelinker/search/dim_selection/runner.py
index 3538f6a..0f9967c 100644
--- a/pelinker/dim_selection/runner.py
+++ b/pelinker/search/dim_selection/runner.py
@@ -16,8 +16,8 @@
 )
 from rich.table import Table
 
-from pelinker.analysis import pooled_min_cluster_size_from_metrics_dfs
-from pelinker.dim_selection.checkpoint import (
+from pelinker.search.grid_solver import pooled_grid_solve_from_metrics_dfs
+from pelinker.search.dim_selection.checkpoint import (
     DEFAULT_CHECKPOINT_NAME,
     compute_run_fingerprint,
     fingerprint_config_from_cli,
@@ -27,7 +27,7 @@
     record_failure,
     save_checkpoint_atomic,
 )
-from pelinker.dim_selection.grids import (
+from pelinker.search.dim_selection.grids import (
     DEFAULT_PCA_GRID,
     DEFAULT_UMAP_GRID,
     cell_key,
@@ -38,36 +38,41 @@
     pick_winner_row,
     refine_cells,
 )
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.dim_selection.summary import (
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.search.dim_selection.summary import (
     render_dim_selection_summary,
     results_dataframe_from_summaries,
 )
-from pelinker.grid_export import grid_export_rows_from_report
-from pelinker.model_selection.artifacts import (
+from pelinker.search.grid_export import grid_export_rows_from_report
+from pelinker.search.model_selection.artifacts import (
     merge_new_frames_into_fine_metadata_jsonl,
     merge_new_frames_into_per_sample_grid_csv,
+    append_sample_labels_parquet,
     merge_new_frames_into_screener_eval_jsonl,
     per_datapoint_scores_df,
 )
-from pelinker.model_selection.fine_metadata import clustering_metadata_df
-from pelinker.model_selection.fusion import clustering_optimization_config_for_run
-from pelinker.ops import parse_model_filename
+from pelinker.search.model_selection.fine_metadata import clustering_metadata_df
+from pelinker.search.model_selection.fusion import (
+    clustering_optimization_config_for_run,
+)
+from pelinker.data.tables import parse_model_filename
 from pelinker.plotting import plot_metrics, plot_metrics_with_error_bars
-from pelinker.reporting import (
+from pelinker.reports.paths import (
     CLUSTERING_SEARCH_FINE_METADATA_BASENAME,
+    CLUSTERING_SEARCH_SAMPLE_LABELS_BASENAME,
     CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME,
     FINE_SCREENER_EVAL_BASENAME,
-    ModelSelectionReport,
-    summarize_clustering_reports_for_search,
 )
-from pelinker.sampling import draw_selection_sample
-from pelinker.selected_hyperparameters import (
+from pelinker.reports.schema import ModelSelectionReport
+from pelinker.reports.summary import summarize_clustering_reports_for_search
+from pelinker.search.sampling import draw_selection_sample
+from pelinker.search.selected_hyperparameters import (
     SelectedHyperparameters,
     write_selected_hyperparameters,
 )
-from pelinker.selection import evaluate_selection_sample, load_selection_frame
-from pelinker.transform import TransformConfig
+from pelinker.search.selection import evaluate_selection_sample, load_selection_frame
+from pelinker.clustering.transform import TransformConfig
+from pelinker.kb.classes import ClassView
 
 
 def _write_handoff(
@@ -80,6 +85,7 @@ def _write_handoff(
     manifold_kind: str,
     umap_n_neighbors: int | None,
     run_fingerprint: str,
+    class_view: str = "raw",
 ) -> None:
     """Emit ``selected_hyperparameters.json`` so ``pelinker-fit`` can read the winner."""
     chosen = payload.get("chosen")
@@ -107,6 +113,7 @@ def _write_handoff(
         umap_n_neighbors=umap_n_neighbors,
         run_fingerprint=run_fingerprint,
         outer_score=chosen.get("outer_score"),
+        class_view=class_view,
     )
     out = write_selected_hyperparameters(selected, report_path)
     console.print(
@@ -207,11 +214,14 @@ def run_dim_selection(
     max_mentions_per_entity: int | None = None,
     max_mentions_negative: int | None = None,
     mention_cap_seed: int = 13,
+    class_view: ClassView = "raw",
+    class_kb_path: pathlib.Path | None = None,
+    persist_labels: bool = False,
 ) -> None:
     """
     Search ``(pca_components, umap_dim)`` for one embedding parquet.
 
-    **Inner** ``min_cluster_size`` (MCS) uses ``grid_objective=dbcv_ari_mean_minmax``.
+    **Inner** ``min_cluster_size`` (MCS) uses ``grid_objective=dbcv_ari_geomean``.
     **Outer** cell ranking uses the same DBCV+ARI pooling across candidate cells
     (``outer_score``); ``best_score`` remains mean DBCV for heatmaps.
     """
@@ -243,8 +253,14 @@ def run_dim_selection(
     detail_path = report_path / CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME
     fine_metadata_path = report_path / CLUSTERING_SEARCH_FINE_METADATA_BASENAME
     fine_screener_eval_path = report_path / FINE_SCREENER_EVAL_BASENAME
+    sample_labels_path = report_path / CLUSTERING_SEARCH_SAMPLE_LABELS_BASENAME
     if not resume:
-        for artifact in (detail_path, fine_metadata_path, fine_screener_eval_path):
+        for artifact in (
+            detail_path,
+            fine_metadata_path,
+            fine_screener_eval_path,
+            sample_labels_path,
+        ):
             try:
                 if artifact.exists():
                     artifact.unlink()
@@ -277,6 +293,8 @@ def run_dim_selection(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=mention_cap_seed,
+        class_view=class_view,
+        class_kb_path=class_kb_path,
     )
     run_fingerprint = compute_run_fingerprint(fp_payload)
 
@@ -331,6 +349,8 @@ def run_dim_selection(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=mention_cap_seed,
+        class_view=class_view,
+        class_kb_path=class_kb_path,
     )
 
     console.print(
@@ -375,6 +395,7 @@ def evaluate_cell(
         grid_report_samples: list[tuple[int, ModelSelectionReport]] = []
         fine_frames: list[pd.DataFrame] = []
         screener_frames: list[pd.DataFrame] = []
+        label_frames: list[pd.DataFrame] = []
 
         for sample_idx in range(n_sample):
             try:
@@ -408,6 +429,12 @@ def evaluate_cell(
                         sample_idx=sample_idx,
                     )
                 )
+                if persist_labels and report.assignments is not None:
+                    labels_df = report.assignments.copy()
+                    labels_df["sample_idx"] = sample_idx
+                    labels_df["pca_components"] = pca_k
+                    labels_df["umap_dim"] = umap_d
+                    label_frames.append(labels_df)
                 if report.screener_oos_datapoints is not None:
                     screener_frames.append(
                         per_datapoint_scores_df(
@@ -429,10 +456,11 @@ def evaluate_cell(
             )
             return None
 
-        pooled_mcs, _ = pooled_min_cluster_size_from_metrics_dfs(
+        grid_solved = pooled_grid_solve_from_metrics_dfs(
             all_metrics_dfs,
             optimization_config,
         )
+        pooled_mcs = grid_solved.chosen_min_cluster_size
         grid_batch = [
             grid_export_rows_from_report(
                 report,
@@ -449,12 +477,15 @@ def evaluate_cell(
             merge_new_frames_into_screener_eval_jsonl(
                 fine_screener_eval_path, screener_frames
             )
+        if label_frames:
+            append_sample_labels_parquet(sample_labels_path, label_frames)
 
         if len(file_metrics) > 1:
             plot_metrics_with_error_bars(
                 file_metrics,
                 report_path / f"{resolved_model}_{export_layer}_error_bars.png",
                 chosen_min_cluster_size=float(pooled_mcs),
+                grid_solve=grid_solved,
             )
         else:
             plot_metrics(
@@ -585,6 +616,7 @@ def evaluate_cell(
         manifold_kind=manifold_kind,
         umap_n_neighbors=umap_n_neighbors,
         run_fingerprint=run_fingerprint,
+        class_view=class_view,
     )
 
     table = Table(title="Dim selection results (outer DBCV+ARI at pooled MCS)")
@@ -595,12 +627,12 @@ def evaluate_cell(
     table.add_column("ari")
     table.add_column("best_size")
     if not df_results.empty:
-        from pelinker.clustering_search_ranking import (
+        from pelinker.clustering.ranking import (
             OUTER_SCORE_COL,
             attach_outer_scores,
         )
 
-        show = attach_outer_scores(df_results, use_minmax=True).sort_values(
+        show = attach_outer_scores(df_results).sort_values(
             by=[OUTER_SCORE_COL, "outer_score_std", "pca_components", "umap_dim"],
             ascending=[False, True, True, True],
             kind="mergesort",
@@ -631,7 +663,7 @@ def evaluate_cell(
         )
     console.print(
         "[dim]MCS = min_cluster_size. "
-        "Inner MCS: DBCV+ARI (dbcv_ari_mean_minmax). "
-        "Outer ranking: DBCV+ARI (minmax across cells). "
+        "Inner MCS: DBCV+ARI (dbcv_ari_geomean, paired 1-SE). "
+        "Outer ranking: DBCV+ARI (geomean, per-cell). "
         "best_score remains mean DBCV.[/dim]"
     )
diff --git a/pelinker/dim_selection/summary.py b/pelinker/search/dim_selection/summary.py
similarity index 95%
rename from pelinker/dim_selection/summary.py
rename to pelinker/search/dim_selection/summary.py
index 3bf2f85..f188582 100644
--- a/pelinker/dim_selection/summary.py
+++ b/pelinker/search/dim_selection/summary.py
@@ -12,18 +12,18 @@
 import seaborn as sns
 from mpl_toolkits.mplot3d import Axes3D  # noqa: F401  # registers 3d projection
 
-from pelinker.clustering_search_ranking import (
+from pelinker.clustering.ranking import (
     METRICS_TWO_LEVEL_DOC,
     OUTER_SCORE_COL,
     attach_outer_scores,
 )
-from pelinker.dim_selection.grids import pick_winner_row
-from pelinker.grid_export import select_grid_points_at_chosen_min_cluster_size
+from pelinker.search.dim_selection.grids import pick_winner_row
+from pelinker.search.grid_export import select_grid_points_at_chosen_min_cluster_size
 from pelinker.plotting import _save_figure_multi_format, plot_dbcv_vs_ari_from_grid
 
 DIM_SELECTION_RESULTS_CSV_BASENAME = "dim_selection.results.csv"
 DIM_SELECTION_SUMMARY_JSON_BASENAME = "dim_selection.summary.json"
-DIM_SELECTION_SUMMARY_JSON_SCHEMA = "pelinker.dim_selection.summary.v1"
+DIM_SELECTION_SUMMARY_JSON_SCHEMA = "pelinker.search.dim_selection.summary.v1"
 
 # Stem basenames (PNG + PDF siblings via ``_save_figure_multi_format``).
 DIM_SELECTION_DBCV_HEATMAP_STEM = "dim.dbcv.heatmap"
@@ -64,7 +64,7 @@ def results_dataframe_from_summaries(
 
 def write_results_csv(df: pd.DataFrame, report_path: pathlib.Path) -> pathlib.Path:
     out = report_path / DIM_SELECTION_RESULTS_CSV_BASENAME
-    scored = attach_outer_scores(df, use_minmax=True) if not df.empty else df
+    scored = attach_outer_scores(df) if not df.empty else df
     tmp = out.with_suffix(out.suffix + ".tmp")
     scored.to_csv(tmp, index=False)
     tmp.replace(out)
@@ -123,12 +123,12 @@ def _plot_dim_heatmap(
 
 def write_dim_heatmaps(df: pd.DataFrame, report_path: pathlib.Path) -> list[str]:
     written: list[str] = []
-    scored = attach_outer_scores(df, use_minmax=True) if not df.empty else df
+    scored = attach_outer_scores(df) if not df.empty else df
     written.extend(
         _plot_dim_heatmap(
             scored,
             metric=OUTER_SCORE_COL,
-            metric_label="Outer DBCV+ARI (minmax)",
+            metric_label="Outer DBCV+ARI (geomean)",
             output_path=report_path / DIM_SELECTION_OUTER_HEATMAP_STEM,
         )
     )
@@ -156,7 +156,7 @@ def write_dim_outer_surface(df: pd.DataFrame, report_path: pathlib.Path) -> list
     """Pseudo-3D surface of ``outer_score`` over PCA × UMAP (PNG + PDF)."""
     if df.empty or "pca_components" not in df.columns or "umap_dim" not in df.columns:
         return []
-    scored = attach_outer_scores(df, use_minmax=True)
+    scored = attach_outer_scores(df)
     if OUTER_SCORE_COL not in scored.columns or scored[OUTER_SCORE_COL].isna().all():
         return []
 
@@ -200,7 +200,7 @@ def write_dim_outer_surface(df: pd.DataFrame, report_path: pathlib.Path) -> list
             alpha=0.92,
             antialiased=True,
         )
-        fig.colorbar(surf, ax=ax, shrink=0.65, label="Outer DBCV+ARI (minmax)")
+        fig.colorbar(surf, ax=ax, shrink=0.65, label="Outer DBCV+ARI (geomean)")
     else:
         xs = agg["pca_components"].to_numpy(dtype=np.float64)
         ys = agg["umap_dim"].to_numpy(dtype=np.float64)
@@ -217,7 +217,7 @@ def write_dim_outer_surface(df: pd.DataFrame, report_path: pathlib.Path) -> list
                 linewidth=0.2,
                 alpha=0.92,
             )
-            fig.colorbar(surf, ax=ax, shrink=0.65, label="Outer DBCV+ARI (minmax)")
+            fig.colorbar(surf, ax=ax, shrink=0.65, label="Outer DBCV+ARI (geomean)")
 
     ax.scatter(
         [win_pca],
@@ -232,7 +232,7 @@ def write_dim_outer_surface(df: pd.DataFrame, report_path: pathlib.Path) -> list
     ax.set_xlabel("pca_components")
     ax.set_ylabel("umap_dim")
     ax.set_zlabel("outer_score")
-    ax.set_title("Outer DBCV+ARI surface (minmax)")
+    ax.set_title("Outer DBCV+ARI surface (geomean)")
     ax.legend(loc="upper left")
     fig.tight_layout()
     try:
diff --git a/pelinker/grid_export.py b/pelinker/search/grid_export.py
similarity index 92%
rename from pelinker/grid_export.py
rename to pelinker/search/grid_export.py
index f14d315..fa63b59 100644
--- a/pelinker/grid_export.py
+++ b/pelinker/search/grid_export.py
@@ -9,9 +9,10 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.clustering_grid import SmoothedGridOptimumResult
-from pelinker.config import ClusteringOptimizationConfig
-from pelinker.reporting import ModelSelectionReport, _json_normalize
+from pelinker.clustering.grid import SmoothedGridOptimumResult
+from pelinker.core.config import ClusteringOptimizationConfig
+from pelinker.reports.io import _json_normalize
+from pelinker.reports.schema import ModelSelectionReport
 
 GRID_COL_CHOSEN_MIN_CLUSTER_SIZE = "chosen_min_cluster_size"
 GRID_COL_N_ROWS_REALIZED = "n_rows_realized"
@@ -67,7 +68,7 @@ def grid_export_rows_from_report(
     ``chosen_min_cluster_size`` is the pooled consensus hyperparameter for the
     (model, layer) combination; it is duplicated on every grid row for that sample.
     ``n_rows_realized`` is this draw's actual mention-row count — the scale the chosen
-    hyperparameter is only meaningful against (see :mod:`pelinker.scaling`).
+    hyperparameter is only meaningful against (see :mod:`pelinker.core.scaling`).
     """
     return report.metrics_df.assign(
         model=model,
@@ -192,12 +193,12 @@ def _solver_config_snapshot(
         "clustering_sample_rows": config.clustering_sample_rows,
         "base_seed": config.base_seed,
         "grid_objective": config.grid_objective,
-        "optimization_method": config.optimization_method,
         "grid_cluster_count_reward": config.grid_cluster_count_reward,
         "grid_n_entities": config.grid_n_entities,
         "grid_smooth_window": config.grid_smooth_window,
-        "grid_plateau_fraction": config.grid_plateau_fraction,
-        "grid_derivative_rel_tol": config.grid_derivative_rel_tol,
+        "grid_one_se_k": config.grid_one_se_k,
+        "grid_one_se_contiguous": config.grid_one_se_contiguous,
+        "grid_one_se_min_samples": config.grid_one_se_min_samples,
     }
 
 
@@ -212,15 +213,21 @@ def _solved_combo_to_jsonable(
         "layer": layer,
         "chosen_min_cluster_size": solved.chosen_min_cluster_size,
         "selection": solved.selection,
+        "argmax_min_cluster_size": solved.argmax_min_cluster_size,
         "score_mean_at_chosen": solved.score_mean_at_chosen,
         "score_std_at_chosen": solved.score_std_at_chosen,
         "n_clusters_mean_at_chosen": solved.n_clusters_mean_at_chosen,
+        "one_se_k": solved.one_se_k,
+        "n_samples": solved.n_samples,
+        "n_clipped": solved.n_clipped,
         "solver_config": _solver_config_snapshot(config),
         "grid_curve": {
             "min_cluster_size": list(solved.x),
             "y_objective": list(solved.y_objective),
             "y_cluster_term": list(solved.y_cluster_term),
             "y_smooth": list(solved.y_smooth),
+            "y_se": list(solved.y_se),
+            "y_eligible": list(solved.y_eligible),
         },
     }
 
diff --git a/pelinker/search/grid_solver.py b/pelinker/search/grid_solver.py
new file mode 100644
index 0000000..5ebbe94
--- /dev/null
+++ b/pelinker/search/grid_solver.py
@@ -0,0 +1,193 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from dataclasses import replace
+
+import pandas as pd
+from pelinker.clustering.grid import (
+    SmoothedGridOptimumResult,
+    solve_optimal_min_cluster_size_from_metrics_dfs,
+)
+from pelinker.search.grid_export import per_combo_metrics_from_grid
+
+from pelinker.core.config import (
+    ClusteringOptimizationConfig,
+    GridObjectiveSpec,
+)
+
+"""Resolve a chosen min_cluster_size from exported per-sample grid metrics.
+
+Search-layer policy on top of :mod:`pelinker.clustering.grid`: pooling across
+samples, applying config overrides, and deciding when a grid solve is warranted.
+"""
+
+
+def pooled_grid_solve_from_metrics_dfs(
+    metrics_dfs: Sequence[pd.DataFrame],
+    optimization_config: ClusteringOptimizationConfig | None = None,
+) -> SmoothedGridOptimumResult:
+    """
+    After all bootstrap samples have run a min_cluster_size grid, solve for the consensus
+    ``min_cluster_size`` and return full grid diagnostics (including the ``y_objective`` curve).
+
+    The per-sample frames are passed through un-pooled: every grid point was evaluated on
+    the same samples, so the solver can use *paired* standard errors, which are far tighter
+    and far more stable than pooling first would allow.
+    """
+    if not metrics_dfs:
+        raise ValueError("metrics_dfs must be non-empty")
+    config = optimization_config or ClusteringOptimizationConfig()
+    return solve_optimal_min_cluster_size_from_metrics_dfs(
+        list(metrics_dfs),
+        objective=config.grid_objective,
+        smooth_window=config.grid_smooth_window,
+        one_se_k=config.grid_one_se_k,
+        one_se_contiguous=config.grid_one_se_contiguous,
+        one_se_min_samples=config.grid_one_se_min_samples,
+        cluster_count_reward=config.grid_cluster_count_reward,
+        n_entities=config.grid_n_entities,
+    )
+
+
+def pooled_min_cluster_size_from_metrics_dfs(
+    metrics_dfs: Sequence[pd.DataFrame],
+    optimization_config: ClusteringOptimizationConfig | None = None,
+) -> tuple[int, float]:
+    """
+    Convenience wrapper returning only ``(chosen_min_cluster_size, raw objective mean there)``.
+    The objective is set by ``ClusteringOptimizationConfig.grid_objective`` (default: the
+    clipped geometric mean of DBCV and ARI).
+
+    Prefer :func:`pooled_grid_solve_from_metrics_dfs` when the caller also plots the grid —
+    it returns the full curve, and dropping it is what leaves the objective panel blank.
+    """
+    solved = pooled_grid_solve_from_metrics_dfs(metrics_dfs, optimization_config)
+    return solved.chosen_min_cluster_size, solved.score_mean_at_chosen
+
+
+def grid_solver_config(
+    optimization_config: ClusteringOptimizationConfig | None,
+    *,
+    grid_cluster_count_reward: float | None = None,
+    grid_n_entities: int | None = None,
+    grid_objective: GridObjectiveSpec | None = None,
+    grid_one_se_k: float | None = None,
+) -> ClusteringOptimizationConfig:
+    """Merge optional solver overrides into ``optimization_config`` (or defaults)."""
+    base = optimization_config or ClusteringOptimizationConfig()
+    overrides: dict[str, object] = {}
+    if grid_cluster_count_reward is not None:
+        overrides["grid_cluster_count_reward"] = grid_cluster_count_reward
+    if grid_n_entities is not None:
+        overrides["grid_n_entities"] = grid_n_entities
+    if grid_objective is not None:
+        overrides["grid_objective"] = grid_objective
+    if grid_one_se_k is not None:
+        overrides["grid_one_se_k"] = grid_one_se_k
+    return replace(base, **overrides) if overrides else base
+
+
+def grid_solver_overrides_active(
+    *,
+    optimization_config: ClusteringOptimizationConfig | None,
+    grid_cluster_count_reward: float | None,
+    grid_n_entities: int | None,
+    grid_objective: GridObjectiveSpec | None,
+    grid_one_se_k: float | None,
+) -> bool:
+    """True when any solver knob was supplied, i.e. a re-solve was explicitly requested."""
+    return any(
+        v is not None
+        for v in (
+            optimization_config,
+            grid_cluster_count_reward,
+            grid_n_entities,
+            grid_objective,
+            grid_one_se_k,
+        )
+    )
+
+
+def should_resolve_chosen_min_cluster_size(
+    *,
+    chosen_min_cluster_size: float | None,
+    optimization_config: ClusteringOptimizationConfig | None,
+    grid_cluster_count_reward: float | None,
+    grid_n_entities: int | None,
+    grid_objective: GridObjectiveSpec | None,
+    grid_one_se_k: float | None,
+) -> bool:
+    """Re-solve only when no chosen value is on hand *and* solver knobs were supplied."""
+    if chosen_min_cluster_size is not None:
+        return False
+    return grid_solver_overrides_active(
+        optimization_config=optimization_config,
+        grid_cluster_count_reward=grid_cluster_count_reward,
+        grid_n_entities=grid_n_entities,
+        grid_objective=grid_objective,
+        grid_one_se_k=grid_one_se_k,
+    )
+
+
+def solve_pooled_grid_from_metrics_list(
+    metrics_list: list[pd.DataFrame],
+    optimization_config: ClusteringOptimizationConfig | None = None,
+    *,
+    grid_cluster_count_reward: float | None = None,
+    grid_n_entities: int | None = None,
+    grid_objective: GridObjectiveSpec | None = None,
+    grid_one_se_k: float | None = None,
+) -> SmoothedGridOptimumResult:
+    """Pooled grid solve on per-sample metric tables; returns full diagnostics."""
+    cfg = grid_solver_config(
+        optimization_config,
+        grid_cluster_count_reward=grid_cluster_count_reward,
+        grid_n_entities=grid_n_entities,
+        grid_objective=grid_objective,
+        grid_one_se_k=grid_one_se_k,
+    )
+    return pooled_grid_solve_from_metrics_dfs(metrics_list, cfg)
+
+
+def solve_pooled_grid_by_combo_from_grid(
+    df_grid: pd.DataFrame,
+    optimization_config: ClusteringOptimizationConfig | None = None,
+    *,
+    grid_cluster_count_reward: float | None = None,
+    grid_n_entities: int | None = None,
+    grid_objective: GridObjectiveSpec | None = None,
+    grid_one_se_k: float | None = None,
+) -> dict[tuple[str, str], SmoothedGridOptimumResult]:
+    """Pooled grid solve per (model, layer) from a grid export CSV frame."""
+    cfg = grid_solver_config(
+        optimization_config,
+        grid_cluster_count_reward=grid_cluster_count_reward,
+        grid_n_entities=grid_n_entities,
+        grid_objective=grid_objective,
+        grid_one_se_k=grid_one_se_k,
+    )
+    return {
+        combo: pooled_grid_solve_from_metrics_dfs(metrics_list, cfg)
+        for combo, metrics_list in per_combo_metrics_from_grid(df_grid).items()
+    }
+
+
+def resolve_chosen_min_cluster_size_by_combo_from_grid(
+    df_grid: pd.DataFrame,
+    optimization_config: ClusteringOptimizationConfig | None = None,
+    *,
+    grid_cluster_count_reward: float | None = None,
+    grid_n_entities: int | None = None,
+    grid_objective: GridObjectiveSpec | None = None,
+    grid_one_se_k: float | None = None,
+) -> dict[tuple[str, str], int]:
+    """Re-solve ``chosen_min_cluster_size`` per (model, layer) from a grid export CSV frame."""
+    solved = solve_pooled_grid_by_combo_from_grid(
+        df_grid,
+        optimization_config,
+        grid_cluster_count_reward=grid_cluster_count_reward,
+        grid_n_entities=grid_n_entities,
+        grid_objective=grid_objective,
+        grid_one_se_k=grid_one_se_k,
+    )
+    return {combo: result.chosen_min_cluster_size for combo, result in solved.items()}
diff --git a/pelinker/model_selection/__init__.py b/pelinker/search/model_selection/__init__.py
similarity index 74%
rename from pelinker/model_selection/__init__.py
rename to pelinker/search/model_selection/__init__.py
index b81d79d..51ac4b1 100644
--- a/pelinker/model_selection/__init__.py
+++ b/pelinker/search/model_selection/__init__.py
@@ -1,7 +1,7 @@
 """Model selection over embedding combinations (grid search + fusion)."""
 
-from pelinker.model_selection.runner import run_model_selection
-from pelinker.model_selection.summary import (
+from pelinker.search.model_selection.runner import run_model_selection
+from pelinker.search.model_selection.summary import (
     SummaryFigureRenderResult,
     build_model_selection_summary_payload,
     render_model_selection_summary_figures,
diff --git a/pelinker/model_selection/artifacts.py b/pelinker/search/model_selection/artifacts.py
similarity index 80%
rename from pelinker/model_selection/artifacts.py
rename to pelinker/search/model_selection/artifacts.py
index efb01db..641214a 100644
--- a/pelinker/model_selection/artifacts.py
+++ b/pelinker/search/model_selection/artifacts.py
@@ -6,9 +6,9 @@
 
 import pandas as pd
 
-from pelinker.clustering_search_ranking import outer_score_from_summary_row
-from pelinker.grid_export import GRID_EXPORT_ID_COLUMNS, grid_export_column_order
-from pelinker.model_selection_checkpoint import (
+from pelinker.clustering.ranking import outer_score_from_summary_row
+from pelinker.search.grid_export import GRID_EXPORT_ID_COLUMNS, grid_export_column_order
+from pelinker.search.model_selection_checkpoint import (
     FailureRecord,
     ModelSelectionCheckpoint,
     model_layer_from_singleton_key,
@@ -16,11 +16,8 @@
     score_by_model_layer_from_checkpoint,
     utc_now_iso,
 )
-from pelinker.reporting import (
-    ClusteringSearchSummaryRow,
-    PerDatapointScores,
-    clustering_search_summary_row_from_flat_dict,
-)
+from pelinker.reports.schema import ClusteringSearchSummaryRow, PerDatapointScores
+from pelinker.reports.summary import clustering_search_summary_row_from_flat_dict
 
 id_columns = list(GRID_EXPORT_ID_COLUMNS)
 
@@ -157,6 +154,46 @@ def merge_new_frames_into_screener_eval_jsonl(
     tmp.replace(path)
 
 
+def append_sample_labels_parquet(
+    path: pathlib.Path,
+    new_frames: list[pd.DataFrame],
+) -> None:
+    """Accumulate per-bootstrap cluster assignments for stability analysis.
+
+    Appends rather than overwrites so a resumed or multi-cell run keeps every draw, and
+    de-duplicates on the mention's identity plus the draw and dims it came from — the
+    same row re-clustered in a later cell is a *different* observation, the same row in
+    the same cell is a repeat.
+    """
+    if not new_frames:
+        return
+    new_df = pd.concat(new_frames, ignore_index=True)
+    if new_df.empty:
+        return
+    prior = pd.read_parquet(path) if path.exists() else None
+    merged = (
+        pd.concat([prior, new_df], ignore_index=True) if prior is not None else new_df
+    )
+    dup_subset = [
+        c
+        for c in (
+            "pmid",
+            "itext",
+            "a_abs",
+            "b_abs",
+            "sample_idx",
+            "pca_components",
+            "umap_dim",
+        )
+        if c in merged.columns
+    ]
+    if dup_subset:
+        merged = merged.drop_duplicates(subset=dup_subset, keep="last")
+    tmp = path.with_name(path.name + ".tmp")
+    merged.to_parquet(tmp, index=False)
+    tmp.replace(path)
+
+
 def per_datapoint_scores_df(
     scores: PerDatapointScores,
     *,
diff --git a/pelinker/model_selection/fine_metadata.py b/pelinker/search/model_selection/fine_metadata.py
similarity index 99%
rename from pelinker/model_selection/fine_metadata.py
rename to pelinker/search/model_selection/fine_metadata.py
index 91adcd2..4e7cb6e 100644
--- a/pelinker/model_selection/fine_metadata.py
+++ b/pelinker/search/model_selection/fine_metadata.py
@@ -8,7 +8,7 @@
 import pandas as pd
 
 from pelinker.plotting import plot_pca_quality_pairgrid
-from pelinker.reporting import ModelSelectionReport
+from pelinker.reports.schema import ModelSelectionReport
 
 
 def validated_oov_label_series(
diff --git a/pelinker/model_selection/fusion.py b/pelinker/search/model_selection/fusion.py
similarity index 88%
rename from pelinker/model_selection/fusion.py
rename to pelinker/search/model_selection/fusion.py
index c834c80..86edf43 100644
--- a/pelinker/model_selection/fusion.py
+++ b/pelinker/search/model_selection/fusion.py
@@ -9,15 +9,13 @@
 import pandas as pd
 from numpy.random import RandomState
 
-from pelinker.clustering_search_ranking import outer_score_from_summary_row
-from pelinker.config import ClusteringOptimizationConfig, NegativeScreenerConfig
-from pelinker.model_selection_checkpoint import combination_key_from_members
-from pelinker.reporting import (
-    ClusteringSearchSummaryRow,
-    ModelSelectionReport,
-)
-from pelinker.selection import evaluate_selection_from_paths
-from pelinker.transform import TransformConfig
+from pelinker.clustering.ranking import outer_score_from_summary_row
+from pelinker.core.config import ClusteringOptimizationConfig, NegativeScreenerConfig
+from pelinker.search.model_selection_checkpoint import combination_key_from_members
+from pelinker.reports.schema import ClusteringSearchSummaryRow, ModelSelectionReport
+from pelinker.search.selection import evaluate_selection_from_paths
+from pelinker.clustering.transform import TransformConfig
+from pelinker.kb.classes import ClassView
 
 
 def path_by_model_layer(
@@ -42,6 +40,8 @@ def clustering_optimization_config_for_run(
     max_mentions_per_entity: int | None,
     max_mentions_negative: int | None,
     mention_cap_seed: int,
+    class_view: ClassView = "raw",
+    class_kb_path: pathlib.Path | None = None,
 ) -> ClusteringOptimizationConfig:
     kind: Literal["lda", "svm"] = "svm" if screener_kind == "svm" else "lda"
     ns = NegativeScreenerConfig(kind=kind, negative_label=negative_label)
@@ -59,7 +59,8 @@ def clustering_optimization_config_for_run(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=mention_cap_seed,
-        optimization_method="mean",
+        class_view=class_view,
+        class_kb_path=None if class_kb_path is None else str(class_kb_path),
         ambient_screener=ns,
     )
 
diff --git a/pelinker/model_selection/runner.py b/pelinker/search/model_selection/runner.py
similarity index 95%
rename from pelinker/model_selection/runner.py
rename to pelinker/search/model_selection/runner.py
index 171bb65..8ea1978 100644
--- a/pelinker/model_selection/runner.py
+++ b/pelinker/search/model_selection/runner.py
@@ -18,18 +18,18 @@
 )
 from rich.table import Table
 
-from pelinker.analysis import pooled_min_cluster_size_from_metrics_dfs
-from pelinker.clustering_fusion_ranking import (
+from pelinker.search.grid_solver import pooled_grid_solve_from_metrics_dfs
+from pelinker.clustering.fusion_ranking import (
     singleton_items_by_dbcv_score,
     top_k_fusion_candidates_by_dbcv_proxy,
 )
-from pelinker.clustering_search_ranking import (
+from pelinker.clustering.ranking import (
     OUTER_SCORE_COL,
     attach_outer_scores,
     pick_best_row,
 )
-from pelinker.grid_export import grid_export_rows_from_report
-from pelinker.model_selection.artifacts import (
+from pelinker.search.grid_export import grid_export_rows_from_report
+from pelinker.search.model_selection.artifacts import (
     mark_combination_done,
     merge_new_frames_into_fine_metadata_jsonl,
     merge_new_frames_into_per_sample_grid_csv,
@@ -42,8 +42,8 @@
     singleton_score_by_model_layer_from_checkpoint,
     dedupe_per_sample_grid,
 )
-from pelinker.model_selection.fine_metadata import clustering_metadata_df
-from pelinker.model_selection.fusion import (
+from pelinker.search.model_selection.fine_metadata import clustering_metadata_df
+from pelinker.search.model_selection.fusion import (
     clustering_optimization_config_for_run,
     combo_key_for_results_row,
     materialize_best_report,
@@ -51,8 +51,10 @@
     path_by_model_layer,
     recompute_leaderboard_from_results,
 )
-from pelinker.model_selection.summary import render_model_selection_summary_figures
-from pelinker.model_selection_checkpoint import (
+from pelinker.search.model_selection.summary import (
+    render_model_selection_summary_figures,
+)
+from pelinker.search.model_selection_checkpoint import (
     DEFAULT_CHECKPOINT_NAME,
     RunMode,
     combination_key_from_members,
@@ -64,35 +66,40 @@
     save_checkpoint_atomic,
     utc_now_iso,
 )
-from pelinker.ops import parse_model_filename
+from pelinker.data.tables import parse_model_filename
 from pelinker.plotting import (
     plot_cluster_viz,
     plot_metrics,
     plot_metrics_with_error_bars,
 )
-from pelinker.reporting import (
+from pelinker.reports.io import write_model_selection_run_report_json
+from pelinker.reports.paths import (
     CLUSTERING_SEARCH_FINE_METADATA_BASENAME,
-    FINE_SCREENER_EVAL_BASENAME,
     CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME,
+    FINE_SCREENER_EVAL_BASENAME,
     MODEL_SELECTION_RUN_REPORT_SCHEMA,
+    model_selection_run_report_path,
+)
+from pelinker.reports.schema import (
     ClusteringSearchSummaryRow,
     ModelSelectionReport,
     ModelSelectionRunReport,
+)
+from pelinker.reports.summary import (
     clustering_search_summary_row_from_flat_dict,
-    model_selection_run_report_path,
     summarize_clustering_reports_for_search,
-    write_model_selection_run_report_json,
 )
-from pelinker.sampling import draw_selection_sample
-from pelinker.selected_hyperparameters import (
+from pelinker.search.sampling import draw_selection_sample
+from pelinker.search.selected_hyperparameters import (
     SelectedHyperparameters,
     write_selected_hyperparameters,
 )
-from pelinker.selection import (
+from pelinker.search.selection import (
     evaluate_selection_sample,
     load_selection_frame,
 )
-from pelinker.transform import TransformConfig
+from pelinker.clustering.transform import TransformConfig
+from pelinker.kb.classes import ClassView
 
 
 def json_ready_flat_row(row: ClusteringSearchSummaryRow) -> dict[str, object]:
@@ -135,6 +142,8 @@ def run_model_selection(
     max_mentions_per_entity: int | None,
     max_mentions_negative: int | None,
     mention_cap_seed: int,
+    class_view: ClassView = "raw",
+    class_kb_path: pathlib.Path | None = None,
     manifold_kind: str = "umap",
     umap_n_neighbors: int | None = None,
 ) -> None:
@@ -147,8 +156,8 @@ def run_model_selection(
     ``best_score`` remains mean DBCV), optionally evaluates fused embeddings:
     pairs/triples with the highest sum of singleton outer scores
     (see ``fusion_pairs`` / ``fusion_triples``), then clusters the
-    concatenated mention-level vectors via :func:`~pelinker.selection.load_selection_frame`
-    and per-bootstrap :func:`~pelinker.selection.evaluate_selection_sample`.
+    concatenated mention-level vectors via :func:`~pelinker.search.selection.load_selection_frame`
+    and per-bootstrap :func:`~pelinker.search.selection.evaluate_selection_sample`.
 
     Checkpointing is on by default (``resume``): progress is saved under ``report_path``.
     Use ``resume=False`` to discard the on-disk checkpoint and start from an empty state.
@@ -221,6 +230,8 @@ def run_model_selection(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=mention_cap_seed,
+        class_view=class_view,
+        class_kb_path=class_kb_path,
     )
     run_fingerprint = compute_run_fingerprint(fp_payload)
 
@@ -394,6 +405,8 @@ def run_model_selection(
                     max_mentions_per_entity=max_mentions_per_entity,
                     max_mentions_negative=max_mentions_negative,
                     mention_cap_seed=mention_cap_seed,
+                    class_view=class_view,
+                    class_kb_path=class_kb_path,
                 )
 
                 status_parts_base = [
@@ -489,10 +502,11 @@ def run_model_selection(
                     gc.collect()
 
                 if file_reports:
-                    pooled_mcs, _ = pooled_min_cluster_size_from_metrics_dfs(
+                    grid_solved = pooled_grid_solve_from_metrics_dfs(
                         all_metrics_dfs,
                         optimization_config,
                     )
+                    pooled_mcs = grid_solved.chosen_min_cluster_size
                     grid_batch = [
                         grid_export_rows_from_report(
                             report,
@@ -518,6 +532,7 @@ def run_model_selection(
                             file_metrics,
                             report_path / f"{model}_{layer}_error_bars.png",
                             chosen_min_cluster_size=float(pooled_mcs),
+                            grid_solve=grid_solved,
                         )
                     else:
                         plot_metrics(
@@ -672,6 +687,8 @@ def run_model_selection(
                         max_mentions_per_entity=max_mentions_per_entity,
                         max_mentions_negative=max_mentions_negative,
                         mention_cap_seed=mention_cap_seed,
+                        class_view=class_view,
+                        class_kb_path=class_kb_path,
                     )
 
                     fusion_status_base = (
@@ -756,10 +773,11 @@ def run_model_selection(
                         gc.collect()
 
                     if fusion_reports:
-                        pooled_mcs, _ = pooled_min_cluster_size_from_metrics_dfs(
+                        grid_solved = pooled_grid_solve_from_metrics_dfs(
                             fusion_all_metrics_dfs,
                             optimization_config,
                         )
+                        pooled_mcs = grid_solved.chosen_min_cluster_size
                         fusion_grid_batch = [
                             grid_export_rows_from_report(
                                 report,
@@ -787,6 +805,7 @@ def run_model_selection(
                                 fusion_metrics,
                                 out_metric,
                                 chosen_min_cluster_size=float(pooled_mcs),
+                                grid_solve=grid_solved,
                             )
                         else:
                             plot_metrics(fusion_metrics[0], out_metric)
@@ -878,7 +897,7 @@ def run_model_selection(
     table.add_column("Scr AUC", justify="right", style="white")
     table.add_column("Comb AUC", justify="right", style="white")
 
-    df_table = attach_outer_scores(df_results, use_minmax=True)
+    df_table = attach_outer_scores(df_results)
     for _, row in df_table.iterrows():
         if n_sample > 1:
             best_size_str = f"{int(row['best_size'])} ± {row['best_size_std']:.1f}"
@@ -956,14 +975,13 @@ def run_model_selection(
                 f"[cyan]{fine_screener_eval_path}[/cyan]"
             )
 
-    df_ranked = attach_outer_scores(df_results, use_minmax=True)
+    df_ranked = attach_outer_scores(df_results)
     if df_ranked.empty:
         console.print("[yellow]No completed combinations to rank.[/yellow]")
         return
     top = pick_best_row(
         df_ranked,
         tie_break_cols=("model", "layer"),
-        use_minmax=True,
     )
     top_flat = {str(k): top[k] for k in df_results.columns if k in top}
     top_summary = clustering_search_summary_row_from_flat_dict(top_flat)
@@ -980,8 +998,8 @@ def run_model_selection(
         f"([cyan]{top['model']}[/cyan]/[yellow]{top['layer']}[/yellow])"
     )
     console.print(
-        "[dim]Inner MCS: DBCV+ARI (dbcv_ari_mean_minmax). "
-        "Outer ranking: DBCV+ARI (minmax across candidates). "
+        "[dim]Inner MCS: DBCV+ARI (dbcv_ari_geomean, paired 1-SE). "
+        "Outer ranking: DBCV+ARI (geomean, per-candidate). "
         "best_score column remains mean DBCV.[/dim]"
     )
 
@@ -1001,6 +1019,8 @@ def run_model_selection(
             max_mentions_per_entity=max_mentions_per_entity,
             max_mentions_negative=max_mentions_negative,
             mention_cap_seed=mention_cap_seed,
+            class_view=class_view,
+            class_kb_path=class_kb_path,
         )
         console.print(
             "[cyan]Materializing best clustering report for UMAP (not held in memory)...[/cyan]"
@@ -1155,6 +1175,7 @@ def run_model_selection(
                     ),
                     umap_n_neighbors=umap_n_neighbors,
                     run_fingerprint=run_fingerprint,
+                    class_view=class_view,
                     outer_score=(
                         None
                         if best_overall_score is None
diff --git a/pelinker/model_selection/summary.py b/pelinker/search/model_selection/summary.py
similarity index 96%
rename from pelinker/model_selection/summary.py
rename to pelinker/search/model_selection/summary.py
index 50f5800..b3d7773 100644
--- a/pelinker/model_selection/summary.py
+++ b/pelinker/search/model_selection/summary.py
@@ -9,35 +9,35 @@
 
 import pandas as pd
 
-from pelinker.clustering_search_ranking import (
+from pelinker.clustering.ranking import (
     METRICS_TWO_LEVEL_DOC,
     OUTER_SCORE_COL,
     attach_outer_scores,
     pick_best_row,
 )
-from pelinker.config import ClusteringOptimizationConfig
-from pelinker.grid_export import (
+from pelinker.core.config import ClusteringOptimizationConfig
+from pelinker.search.grid_export import (
     GRID_COL_CHOSEN_MIN_CLUSTER_SIZE,
     apply_chosen_min_cluster_size_to_grid,
     per_combo_metrics_from_grid,
     write_grid_chosen_hyperparameters,
 )
-from pelinker.model_selection.artifacts import (
+from pelinker.search.model_selection.artifacts import (
     dedupe_per_sample_grid,
     read_optional_csv,
     read_optional_jsonl_gzip,
     results_from_checkpoint,
 )
-from pelinker.model_selection.fine_metadata import (
+from pelinker.search.model_selection.fine_metadata import (
     write_pca_quality_pairgrids_from_fine_metadata,
 )
-from pelinker.model_selection.fusion import combo_key_for_results_row
-from pelinker.model_selection_checkpoint import (
+from pelinker.search.model_selection.fusion import combo_key_for_results_row
+from pelinker.search.model_selection_checkpoint import (
     DEFAULT_CHECKPOINT_NAME,
     load_checkpoint,
     utc_now_iso,
 )
-from pelinker.analysis import solve_pooled_grid_by_combo_from_grid
+from pelinker.search.grid_solver import solve_pooled_grid_by_combo_from_grid
 from pelinker.plotting import (
     plot_dbcv_vs_ari_from_grid,
     plot_heatmap,
@@ -46,14 +46,14 @@
     plot_roc_comparison,
     plot_screener_oov_bar,
 )
-from pelinker.reporting import (
+from pelinker.reports.io import write_model_selection_summary_json
+from pelinker.reports.paths import (
     CLUSTERING_SEARCH_FINE_METADATA_BASENAME,
     CLUSTERING_SEARCH_GRID_CHOSEN_JSON_BASENAME,
     CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME,
     FINE_SCREENER_EVAL_BASENAME,
     MODEL_SELECTION_SUMMARY_JSON_SCHEMA,
     model_selection_summary_json_path,
-    write_model_selection_summary_json,
 )
 
 
@@ -150,11 +150,9 @@ def build_model_selection_summary_payload(
     best_outer: dict[str, Any] | None = None
     best_dbcv: dict[str, Any] | None = None
     if not df_heatmap.empty and "best_score" in df_heatmap.columns:
-        scored = attach_outer_scores(df_heatmap, use_minmax=True)
+        scored = attach_outer_scores(df_heatmap)
         try:
-            winner = pick_best_row(
-                scored, tie_break_cols=("model", "layer"), use_minmax=True
-            )
+            winner = pick_best_row(scored, tie_break_cols=("model", "layer"))
             best_outer = _summary_combo_entry_from_row(pd.Series(winner))
             best_outer["rank_metric"] = OUTER_SCORE_COL
             best_outer["rank_value"] = _json_float_metric(winner.get(OUTER_SCORE_COL))
diff --git a/pelinker/model_selection_checkpoint.py b/pelinker/search/model_selection_checkpoint.py
similarity index 94%
rename from pelinker/model_selection_checkpoint.py
rename to pelinker/search/model_selection_checkpoint.py
index 8bda583..12752da 100644
--- a/pelinker/model_selection_checkpoint.py
+++ b/pelinker/search/model_selection_checkpoint.py
@@ -1,7 +1,7 @@
-"""Structured checkpoint I/O for ``pelinker.model_selection`` runs.
+"""Structured checkpoint I/O for ``pelinker.search.model_selection`` runs.
 
 Shared machinery (version, fingerprint, atomic gzip-aware I/O, the fingerprint fields
-common to every search) lives in :mod:`pelinker.checkpoint`; only the
+common to every search) lives in :mod:`pelinker.search.checkpoint_base`; only the
 combination-keyed schema is here.
 """
 
@@ -11,7 +11,7 @@
 from dataclasses import dataclass, field
 from typing import Any, Literal
 
-from pelinker.checkpoint import (
+from pelinker.search.checkpoint_base import (
     CHECKPOINT_VERSION,
     compute_run_fingerprint,
     read_checkpoint_json,
@@ -20,8 +20,9 @@
     utc_now_iso,
     write_checkpoint_json_atomic,
 )
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.reporting import MODEL_SELECTION_CHECKPOINT_BASENAME
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.reports.paths import MODEL_SELECTION_CHECKPOINT_BASENAME
+from pelinker.kb.classes import ClassView
 
 __all__ = [
     "CHECKPOINT_VERSION",
@@ -163,6 +164,8 @@ def fingerprint_config_from_cli(
     max_mentions_per_entity: int | None = None,
     max_mentions_negative: int | None = None,
     mention_cap_seed: int = 13,
+    class_view: ClassView = "raw",
+    class_kb_path: pathlib.Path | None = None,
 ) -> dict[str, Any]:
     return {
         **shared_fingerprint_fields(
@@ -185,6 +188,8 @@ def fingerprint_config_from_cli(
             max_mentions_per_entity=max_mentions_per_entity,
             max_mentions_negative=max_mentions_negative,
             mention_cap_seed=mention_cap_seed,
+            class_view=class_view,
+            class_kb_path=class_kb_path,
         ),
         "input_dir": str(input_dir.expanduser().resolve()),
         "pca_components": pca_components,
diff --git a/pelinker/sampling.py b/pelinker/search/sampling.py
similarity index 98%
rename from pelinker/sampling.py
rename to pelinker/search/sampling.py
index 913ece0..4d9fab0 100644
--- a/pelinker/sampling.py
+++ b/pelinker/search/sampling.py
@@ -5,7 +5,7 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.config import ClusteringOptimizationConfig
+from pelinker.core.config import ClusteringOptimizationConfig
 
 
 def selection_sample_target_size(
diff --git a/pelinker/scale_curve/__init__.py b/pelinker/search/scale_curve/__init__.py
similarity index 83%
rename from pelinker/scale_curve/__init__.py
rename to pelinker/search/scale_curve/__init__.py
index 0258049..040490f 100644
--- a/pelinker/scale_curve/__init__.py
+++ b/pelinker/search/scale_curve/__init__.py
@@ -1,6 +1,6 @@
-"""Sample-size sweep for ``min_cluster_size`` (see :mod:`pelinker.scaling`)."""
+"""Sample-size sweep for ``min_cluster_size`` (see :mod:`pelinker.core.scaling`)."""
 
-from pelinker.scale_curve.runner import (
+from pelinker.search.scale_curve.runner import (
     DEFAULT_RUNGS,
     SCALE_CURVE_JSON_BASENAME,
     SCALE_CURVE_SCHEMA,
diff --git a/pelinker/scale_curve/runner.py b/pelinker/search/scale_curve/runner.py
similarity index 91%
rename from pelinker/scale_curve/runner.py
rename to pelinker/search/scale_curve/runner.py
index 245d740..6435ae8 100644
--- a/pelinker/scale_curve/runner.py
+++ b/pelinker/search/scale_curve/runner.py
@@ -2,9 +2,9 @@
 
 One "rung" is a full inner search (``n_sample`` stratified draws → pooled
 ``min_cluster_size``) at a single ``clustering_sample_rows`` value. Rungs reuse the
-production path exactly — :func:`~pelinker.sampling.draw_selection_sample`,
-:func:`~pelinker.selection.evaluate_selection_sample`,
-:func:`~pelinker.analysis.pooled_min_cluster_size_from_metrics_dfs` — so the measured
+production path exactly — :func:`~pelinker.search.sampling.draw_selection_sample`,
+:func:`~pelinker.search.selection.evaluate_selection_sample`,
+:func:`~pelinker.search.grid_solver.pooled_min_cluster_size_from_metrics_dfs` — so the measured
 exponent describes the real selector, not a reimplementation of it.
 
 The mention frame is loaded and filtered **once**; only the subsample size varies
@@ -23,31 +23,32 @@
 from rich.console import Console
 from rich.table import Table
 
-from pelinker.analysis import pooled_min_cluster_size_from_metrics_dfs
-from pelinker.config import ClusteringOptimizationConfig, TransformConfig
-from pelinker.grid_export import grid_export_rows_from_report
-from pelinker.model_selection.artifacts import (
+from pelinker.search.grid_solver import pooled_min_cluster_size_from_metrics_dfs
+from pelinker.core.config import ClusteringOptimizationConfig, TransformConfig
+from pelinker.search.grid_export import grid_export_rows_from_report
+from pelinker.search.model_selection.artifacts import (
     merge_new_frames_into_per_sample_grid_csv,
 )
-from pelinker.model_selection.fusion import clustering_optimization_config_for_run
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.ops import parse_model_filename
-from pelinker.reporting import (
-    CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME,
-    ModelSelectionReport,
+from pelinker.search.model_selection.fusion import (
+    clustering_optimization_config_for_run,
 )
-from pelinker.sampling import draw_selection_sample
-from pelinker.scaling import (
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.data.tables import parse_model_filename
+from pelinker.reports.paths import CLUSTERING_SEARCH_GRID_PER_SAMPLE_CSV_BASENAME
+from pelinker.reports.schema import ModelSelectionReport
+from pelinker.search.sampling import draw_selection_sample
+from pelinker.core.scaling import (
     MIN_RUNGS_FOR_FIT,
     ScaleCurve,
     ScaleRung,
     fit_scale_curve,
 )
-from pelinker.selection import evaluate_selection_sample, load_selection_frame
+from pelinker.search.selection import evaluate_selection_sample, load_selection_frame
+from pelinker.kb.classes import ClassView
 
 SCALE_CURVE_JSON_BASENAME = "scale_curve.json"
 SCALE_CURVE_FIGURE_BASENAME = "scale_curve"
-SCALE_CURVE_SCHEMA = "pelinker.scale_curve.v1"
+SCALE_CURVE_SCHEMA = "pelinker.search.scale_curve.v1"
 
 DEFAULT_RUNGS: tuple[int, ...] = (10_000, 25_000, 50_000, 100_000)
 
@@ -207,6 +208,8 @@ def run_scale_curve(
     max_mentions_per_entity: int | None = None,
     max_mentions_negative: int | None = None,
     mention_cap_seed: int = 13,
+    class_view: ClassView = "raw",
+    class_kb_path: pathlib.Path | None = None,
 ) -> ScaleCurve | None:
     """Sweep ``clustering_sample_rows`` and fit ``log(MCS*) ~ a + b·log(N)``.
 
@@ -261,6 +264,8 @@ def run_scale_curve(
         max_mentions_per_entity=max_mentions_per_entity,
         max_mentions_negative=max_mentions_negative,
         mention_cap_seed=mention_cap_seed,
+        class_view=class_view,
+        class_kb_path=class_kb_path,
     )
     viz_method = cluster_viz_method.lower()
     if viz_method not in ("pca", "umap"):
diff --git a/pelinker/selected_hyperparameters.py b/pelinker/search/selected_hyperparameters.py
similarity index 90%
rename from pelinker/selected_hyperparameters.py
rename to pelinker/search/selected_hyperparameters.py
index d7381d8..fec1f8c 100644
--- a/pelinker/selected_hyperparameters.py
+++ b/pelinker/search/selected_hyperparameters.py
@@ -9,7 +9,7 @@
 This module defines the small typed record both searches emit and the fit consumes, so
 the transfer is checked rather than remembered. The realized row count travels with it —
 ``min_cluster_size`` is an absolute count and means nothing without the N it was chosen
-at (see :mod:`pelinker.scaling`).
+at (see :mod:`pelinker.core.scaling`).
 """
 
 from __future__ import annotations
@@ -20,7 +20,7 @@
 from typing import Any
 
 SELECTED_HYPERPARAMETERS_BASENAME = "selected_hyperparameters.json"
-SELECTED_HYPERPARAMETERS_SCHEMA = "pelinker.selected_hyperparameters.v1"
+SELECTED_HYPERPARAMETERS_SCHEMA = "pelinker.search.selected_hyperparameters.v1"
 
 
 @dataclass(frozen=True)
@@ -43,6 +43,14 @@ class SelectedHyperparameters:
     umap_n_neighbors: int | None = None
     run_fingerprint: str | None = None
     outer_score: float | None = None
+    class_view: str | None = None
+    """Class view the search's ARI scored against (:mod:`pelinker.kb.classes`). ``None``
+    for a handoff written before views existed, which scored against raw labels."""
+
+    @property
+    def effective_class_view(self) -> str:
+        """The view the search actually used: ``raw`` when none was recorded."""
+        return self.class_view or "raw"
 
     def __post_init__(self) -> None:
         if not self.model:
@@ -75,6 +83,7 @@ def to_jsonable(self) -> dict[str, Any]:
                 None if self.umap_n_neighbors is None else int(self.umap_n_neighbors)
             ),
             "run_fingerprint": self.run_fingerprint,
+            "class_view": self.class_view,
             "outer_score": (
                 None if self.outer_score is None else float(self.outer_score)
             ),
@@ -106,6 +115,7 @@ def from_jsonable(data: dict[str, Any]) -> SelectedHyperparameters:
                 else int(data["umap_n_neighbors"])
             ),
             run_fingerprint=data.get("run_fingerprint"),
+            class_view=data.get("class_view"),
             outer_score=(
                 None if data.get("outer_score") is None else float(data["outer_score"])
             ),
diff --git a/pelinker/selection.py b/pelinker/search/selection.py
similarity index 80%
rename from pelinker/selection.py
rename to pelinker/search/selection.py
index 3e9a4c7..727ee0f 100644
--- a/pelinker/selection.py
+++ b/pelinker/search/selection.py
@@ -14,34 +14,38 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.analysis import (
-    drop_entities_with_few_mentions,
+from pelinker.data.frames import drop_entities_with_few_mentions
+from pelinker.screener.evaluation import (
     evaluate_all_screeners_cv,
-    pooled_min_cluster_size_from_metrics_dfs,
     split_by_negative_label,
 )
-from pelinker.clustering_fit import (
+from pelinker.search.grid_solver import pooled_min_cluster_size_from_metrics_dfs
+from pelinker.clustering.fit import (
     ManifoldTransformerFitResult,
     fit_hdbscan_on_umap,
     fit_transformer_on_manifold,
 )
-from pelinker.clustering_grid import evaluate_cluster_size_grid
-from pelinker.config import (
+from pelinker.clustering.grid import evaluate_cluster_size_grid
+from pelinker.core.config import (
     ClusteringOptimizationConfig,
     ManifoldOovScreenerConfig,
     TransformConfig,
 )
-from pelinker.embedding_fusion import concat_mention_level_embedding_sources
-from pelinker.reporting import (
+from pelinker.data.fusion import concat_mention_level_embedding_sources
+from pelinker.data.frames import entity_negative_label_mask_01, mention_quality_frame
+from pelinker.kb.classes import (
+    ENTITY_CLASS_COLUMN,
+    KbClasses,
+    add_view_columns,
+)
+from pelinker.reports.schema import (
     AllScreenerCvResult,
     ClusteringHyperparameters,
     ModelSelectionReport,
     PerDatapointScores,
-    entity_negative_label_mask_01,
-    mention_quality_frame,
 )
-from pelinker.sampling import cap_mentions_per_entity, draw_selection_sample
-from pelinker.transform import TransformArtifacts, score_transform_artifacts
+from pelinker.search.sampling import cap_mentions_per_entity, draw_selection_sample
+from pelinker.clustering.transform import TransformArtifacts, score_transform_artifacts
 
 
 def load_selection_frame(
@@ -110,7 +114,31 @@ def load_selection_frame(
         )
         if len(frame) == 0:
             return None
-    return frame
+    return apply_class_view(frame, config)
+
+
+def apply_class_view(
+    frame: pd.DataFrame, config: ClusteringOptimizationConfig
+) -> pd.DataFrame:
+    """Add the configured class view's columns (:mod:`pelinker.kb.classes`) to ``frame``.
+
+    ``raw`` leaves the frame untouched, so agreement metrics keep scoring against the
+    matched label. Any other view needs ``config.class_kb_path``.
+    """
+    if config.class_view == "raw":
+        return frame
+    if config.class_kb_path is None:
+        raise ValueError(
+            f"class_view={config.class_view!r} needs class_kb_path (the pairs KB the "
+            "mentions were embedded with); use class_view='raw' for matched labels."
+        )
+    classes = KbClasses.from_csv(config.class_kb_path)
+    return add_view_columns(
+        frame,
+        classes,
+        config.class_view,
+        passthrough_labels=frozenset({config.ambient_screener.negative_label}),
+    )
 
 
 def _prepare_screener_cv_arrays(
@@ -158,7 +186,12 @@ def _build_selection_assignments(
     labels: np.ndarray,
 ) -> pd.DataFrame:
     assignments = dfr_manifold[["entity"]].copy()
-    for optional_col in ["pmid", "mention"]:
+    # ``a_abs``/``b_abs``/``itext`` carry through so a row keeps an identity across
+    # bootstrap draws — ``(pmid, itext, a_abs, b_abs)`` addresses one mention, while
+    # ``(pmid, mention)`` repeats whenever a document mentions a predicate twice. Cluster
+    # identity across draws cannot be measured without it (see
+    # :mod:`pelinker.clustering.stability`).
+    for optional_col in ["pmid", "mention", "itext", "a_abs", "b_abs"]:
         if optional_col in dfr_manifold.columns:
             assignments[optional_col] = dfr_manifold[optional_col]
     assignments["cluster"] = labels.astype(int, copy=False)
@@ -180,10 +213,10 @@ def evaluate_selection_sample(
 
     ``n_clusters_emergent`` in the returned report is at the grid-optimal ``best_size``,
     not an arbitrary ``min_cluster_size``. Compare with ``Linker.fit`` at a fixed MCS via
-    :func:`~pelinker.reporting.n_clusters_at_min_cluster_size` on ``metrics_df``.
+    :func:`~pelinker.reports.schema.n_clusters_at_min_cluster_size` on ``metrics_df``.
 
     When ``all_metrics_dfs`` is provided, appends this sample's grid ``metrics_df``. Run
-    :func:`~pelinker.analysis.pooled_min_cluster_size_from_metrics_dfs` after all bootstraps
+    :func:`~pelinker.search.grid_solver.pooled_min_cluster_size_from_metrics_dfs` after all bootstraps
     for a pooled ``min_cluster_size``.
 
     ``entities_pre_filtered=True`` skips min-mention-per-entity trimming (default when the
@@ -252,9 +285,12 @@ def evaluate_selection_sample(
     if unified is not None:
         all_screener_cv, screener_oos_dp = unified
 
-    umap_clustering_df = artifacts.umap_clustering_df().assign(
-        entity=dfr_manifold["entity"].values
-    )
+    reference_columns = {"entity": dfr_manifold["entity"].values}
+    if ENTITY_CLASS_COLUMN in dfr_manifold.columns:
+        reference_columns[ENTITY_CLASS_COLUMN] = dfr_manifold[
+            ENTITY_CLASS_COLUMN
+        ].values
+    umap_clustering_df = artifacts.umap_clustering_df().assign(**reference_columns)
 
     sizes = list(
         np.arange(
@@ -265,7 +301,7 @@ def evaluate_selection_sample(
     )
     metrics_df = evaluate_cluster_size_grid(
         umap_clustering_df,
-        [c for c in umap_clustering_df.columns if c != "entity"],
+        [c for c in umap_clustering_df.columns if c not in reference_columns],
         sizes,
     )
     if len(metrics_df) == 0:
diff --git a/pelinker/text/__init__.py b/pelinker/text/__init__.py
new file mode 100644
index 0000000..669ca1d
--- /dev/null
+++ b/pelinker/text/__init__.py
@@ -0,0 +1 @@
+"""Tokenization, chunking, transformer loading, and pooled text embeddings."""
diff --git a/pelinker/text/chunking.py b/pelinker/text/chunking.py
new file mode 100644
index 0000000..287948c
--- /dev/null
+++ b/pelinker/text/chunking.py
@@ -0,0 +1,156 @@
+"""Split long documents into encoder-sized chunks."""
+
+import dataclasses
+import re
+from collections import defaultdict
+from dataclasses import field
+
+import torch
+
+from pelinker.core.onto import BaseDataclass
+from pelinker.text.tokenize import map_words_to_tokens_list
+
+
+def split_text_into_batches(text: str, max_length: int) -> list[str]:
+    """Split a single string into chunks no longer than ``max_length`` characters.
+
+    Uses a regex that prefers breaking after whitespace near the limit; a chunk may
+    reach ``max_length`` when no earlier break exists.
+
+    Args:
+        text: Full document or segment to split.
+        max_length: Maximum characters per chunk (typically tokenizer/model limit).
+
+    Returns:
+        Non-empty string segments whose concatenation recovers ``text`` (aside from
+        regex edge cases on pathological input).
+    """
+    pattern = (
+        r"(.{1," + str(max_length - 1) + r"})(\s|$)|(.{1," + str(max_length) + r"})"
+    )
+
+    matches = re.findall(pattern, text)
+    batched = ["".join(parts) for parts in matches]
+    return batched
+
+
+def split_text_into_token_budget(text: str, tokenizer, max_tokens: int) -> list[str]:
+    """Split *text* so each segment encodes to at most *max_tokens* subword tokens.
+
+    Uses a longest-prefix binary search per segment (by character offset), then
+    prefers breaking at the last space still within the token budget. Avoids relying
+    on a fixed character cap, which can exceed the model's tokenizer limit.
+
+    Args:
+        text: Full document string for one logical chunking pass.
+        tokenizer: Hugging Face tokenizer (``encode(..., add_special_tokens=False)``).
+        max_tokens: Maximum subword count per segment (typically ``MAX_LENGTH``).
+
+    Returns:
+        Segments whose concatenation equals *text* exactly (no dropped characters).
+
+    Raises:
+        ValueError: If ``max_tokens < 1``, or a minimal slice still exceeds the budget.
+    """
+    if max_tokens < 1:
+        raise ValueError("max_tokens must be at least 1")
+    if not text:
+        return []
+
+    def n_tokens(segment: str) -> int:
+        return len(tokenizer.encode(segment, add_special_tokens=False))
+
+    chunks: list[str] = []
+    start = 0
+    n = len(text)
+    while start < n:
+        if n_tokens(text[start:n]) <= max_tokens:
+            chunks.append(text[start:n])
+            break
+
+        lo, hi = start + 1, n + 1
+        best = start
+        while lo < hi:
+            mid = (lo + hi) // 2
+            if n_tokens(text[start:mid]) <= max_tokens:
+                best = mid
+                lo = mid + 1
+            else:
+                hi = mid
+        if best <= start:
+            raise ValueError(
+                "a minimal text slice exceeds max_tokens; increase max_length or inspect the tokenizer"
+            )
+
+        end = best
+        sp = text.rfind(" ", start + 1, end)
+        if sp > start and n_tokens(text[start:sp]) <= max_tokens:
+            end = sp
+
+        chunk = text[start:end]
+        chunks.append(chunk)
+        start = end
+
+    return chunks
+
+
+@dataclasses.dataclass
+class ChunkMapper(BaseDataclass):
+    """Maps encoder chunks back to documents and optional pooled span rows.
+
+    When :func:`pelinker.text.embed.texts_to_vrep` runs multiple ``word_modes``, it calls
+    :func:`pelinker.text.embed.render_elementary_tensor_table` repeatedly on the **same**
+    instance. Fields ``text_word_spans_list``, ``token_word_spans_list``,
+    ``tt_expressions``, and ``mapping_table`` therefore reflect **only the last**
+    grouping pass; read per-mode results from :class:`ReportBatch` instead.
+    """
+
+    tensor: torch.Tensor  # n_layers x n_batch x n_len x n_emb - tensor where n_batch dim goes over all chunks
+    chunks: list[str]  # flat list of chunks
+    token_spans_list: list[
+        list[tuple[int, int]]
+    ]  # for each chunk contains a list of token spans
+    it_ic: list[tuple[int, int]]
+    cumulative_lens: list[list[int]]
+    text_word_spans_list: list[list[tuple[int, int]]] | None = None
+    token_word_spans_list: list[list[tuple[int, int]]] | None = None
+    mapping_table: list[tuple[int, int, tuple[int, int], tuple[int, int]]] | None = None
+    text_chunk_map: defaultdict[int, list] = field(
+        default_factory=lambda: defaultdict(list)
+    )
+    tt_expressions: list[torch.Tensor] = field(
+        default_factory=list
+    )  # n_expressions [n_len x n_emb]
+
+    def set_token_word_spans(self, word_int_bounds):
+
+        self.token_word_spans_list, self.text_word_spans_list = (
+            map_words_to_tokens_list(self.token_spans_list, word_int_bounds)
+        )
+
+    def set_mapping_table(self):
+        it_ic = sorted(self.it_ic)
+        self.mapping_table = []
+        self.text_chunk_map = defaultdict(list)
+
+        if self.text_word_spans_list is None:
+            pass
+        for (ichunk, (itext, ichunk_local)), chsp in zip(
+            enumerate(it_ic), self.text_word_spans_list
+        ):
+            self.text_chunk_map[itext].append(ichunk)
+            chunk_offset = self.cumulative_lens[itext][ichunk_local]
+            for a, b in chsp:
+                self.mapping_table += [
+                    (itext, ichunk, (a, b), (a + chunk_offset, b + chunk_offset))
+                ]
+
+    def map_chunk_to_text(self, itext, ichunk_local, a=0):
+        return a + self.cumulative_lens[itext][ichunk_local]
+
+    def ichunk_to_itext_ichunk_local(self, ichunk: int):
+        return self.it_ic[ichunk]
+
+    def ichunk_char_offset(self, ichunk: int):
+        itext, ichunk_local = self.ichunk_to_itext_ichunk_local(ichunk)
+        return self.cumulative_lens[itext][ichunk_local]
diff --git a/pelinker/util.py b/pelinker/text/embed.py
similarity index 60%
rename from pelinker/util.py
rename to pelinker/text/embed.py
index bce5c21..5a3df5e 100644
--- a/pelinker/util.py
+++ b/pelinker/text/embed.py
@@ -1,141 +1,50 @@
-# pylint: disable=E1120
+"""The embedding pipeline: text -> hidden states -> pooled mention vectors."""
 
-import os
-import re
-from pathlib import Path
+from __future__ import annotations
 
+import dataclasses
+import logging
 from collections.abc import Callable
-from string import punctuation, whitespace
+from functools import reduce
 from typing import List
-from transformers import AutoModel, AutoTokenizer
-from sentence_transformers import SentenceTransformer
+
 import numpy as np
-import torch
 import pandas as pd
-from sklearn.metrics import accuracy_score
-from functools import reduce
-import logging
+import torch
+from spacy.language import Language
 
-from pelinker.onto import (
-    MAX_LENGTH,
-    NEGATIVE_LABEL,
-    ChunkMapper,
-    WordGrouping,
-    SimplifiedToken,
+from pelinker.core.onto import (
+    BaseDataclass,
     Expression,
     ExpressionHolder,
     ExpressionHolderBatch,
-    ReportBatch,
+    MAX_LENGTH,
+    NEGATIVE_LABEL,
+    SimplifiedToken,
+    WordGrouping,
     _wg_for_property,
 )
-from spacy.language import Language
+from pelinker.text.chunking import (
+    ChunkMapper,
+    split_text_into_batches,
+    split_text_into_token_budget,
+)
+from pelinker.text.models import normalize_layers_spec
+from pelinker.text.mentions import (
+    LEXICAL_RULE,
+    keep_one_per_site,
+)
+from pelinker.text.mentions import split_predicate_labels as _split_predicate_labels
+from pelinker.text.predicates import PredicateMatcher, PredicateSpec
+from pelinker.text.tokenize import (
+    text_to_tokens,
+    token_list_with_window,
+    tokens_from_doc,
+)
 
 logger = logging.getLogger(__name__)
 
 
-def expand_config_path(path: str | os.PathLike[str] | None) -> Path | None:
-    """Expand environment variables and ``~`` in config or CLI path strings."""
-    if path is None:
-        return None
-    return Path(os.path.expandvars(os.fspath(path))).expanduser()
-
-
-def load_models(model_type, sentence=False):
-    if model_type == "scibert":
-        spec = "allenai/scibert_scivocab_cased"
-    elif model_type == "biobert":
-        spec = "dmis-lab/biobert-base-cased-v1.2"
-    elif model_type == "pubmedbert":
-        spec = "neuml/pubmedbert-base-embeddings"
-    elif model_type == "biobert-stsb":
-        spec = "pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb"
-    elif model_type == "bert":
-        spec = "google-bert/bert-base-uncased"
-    elif model_type == "bluebert":
-        spec = "bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12"
-    else:
-        raise ValueError(f"{model_type} unsupported")
-    if sentence:
-        tokenizer, model = None, SentenceTransformer(spec)
-    else:
-        tokenizer, model = (
-            AutoTokenizer.from_pretrained(spec),
-            AutoModel.from_pretrained(spec),
-        )
-    return tokenizer, model
-
-
-def layers2str(layers: str | list[int]) -> str:  # used by run/obsolete only
-    if isinstance(layers, str):
-        layers_str = layers
-    else:
-        if any(l0 > 0 for l0 in layers):
-            raise ValueError(f" there are positive layers: {layers}")
-        alayers = sorted([abs(l0) for l0 in layers])
-        layers_str = "".join([str(l0) for l0 in alayers])
-    return layers_str
-
-
-def normalize_layers_spec(
-    layers_spec: str | list[int],
-    *,
-    n_hidden_states: int | None = None,
-) -> list[int]:
-    """Parse and validate indices for the stacked ``hidden_states`` tensor.
-
-    String form follows the same convention as historical :func:`str2layers`: each
-    digit is a distinct layer counted from the end, e.g. ``\"1\"`` → ``[-1]``,
-    ``\"12\"`` → ``[-2, -1]``. Commas in the string are ignored.
-
-    Args:
-        layers_spec: Digit-only string or list of **negative** indices (HF convention).
-        n_hidden_states: If set (length of first dim of stacked hidden states), indices
-            must satisfy ``layer >= -n_hidden_states``.
-
-    Returns:
-        Sorted unique negative layer indices.
-
-    Raises:
-        ValueError: Empty spec, positive indices, ``\"sent\"``, or out-of-range indices.
-    """
-    if isinstance(layers_spec, str):
-        spec = layers_spec.strip()
-        if not spec:
-            raise ValueError("layers_spec string is empty")
-        if spec == "sent":
-            raise ValueError(
-                "layers_spec 'sent' is not valid for transformer hidden-state pooling"
-            )
-        if "," in spec:
-            spec = "".join(spec.split(","))
-        if not spec.isdigit():
-            raise ValueError(
-                f"layers_spec string must be digits only (e.g. '1' or '12'), got {layers_spec!r}"
-            )
-        layers = sorted({-abs(int(ch)) for ch in spec})
-    else:
-        if not layers_spec:
-            raise ValueError("layers_spec list is empty")
-        for i, layer in enumerate(layers_spec):
-            if layer >= 0:
-                raise ValueError(
-                    f"layer index must be negative (HF hidden_states convention), got {layer} at position {i}"
-                )
-        layers = sorted(set(layers_spec))
-    if n_hidden_states is not None:
-        for layer in layers:
-            if layer < -n_hidden_states:
-                raise ValueError(
-                    f"layer {layer} is out of range for {n_hidden_states} stacked hidden states"
-                )
-    return layers
-
-
-def str2layers(layers_spec: str | list[int]) -> list[int]:
-    """Parse layer specification; same rules as :func:`normalize_layers_spec`."""
-    return normalize_layers_spec(layers_spec, n_hidden_states=None)
-
-
 def text_to_tokens_embeddings(
     texts: list[str],
     tokenizer,
@@ -164,7 +73,7 @@ def text_to_tokens_embeddings(
         span pairs from ``offset_mapping`` are dropped).
     """
 
-    encoding = tokenizer.batch_encode_plus(
+    encoding = tokenizer(
         texts,
         max_length=MAX_LENGTH,
         return_offsets_mapping=True,
@@ -209,79 +118,6 @@ def text_to_tokens_embeddings(
     return tt, enu_offsets
 
 
-def map_spans_to_spans_basic(
-    words_boundaries, token_boundaries
-) -> dict[tuple[int, int], list[int]]:
-    """Map each word character span to tokenizer subword indices.
-
-    Both spans use half-open intervals ``[start, end)`` in character offsets. A
-    subword token belongs to a word span iff the two intervals have positive overlap.
-
-    Word spans may overlap (sliding W2/W3 windows). A single forward pointer over
-    tokens is incorrect in that case; each word span is matched independently.
-
-    Args:
-        words_boundaries: Sequence of ``(wa, wb)`` word/window spans.
-        token_boundaries: Sequence of ``(ta, tb)`` subword spans covering the chunk.
-
-    Returns:
-        Dict ``(wa, wb) -> [token_index, ...]`` in ascending token order.
-    """
-
-    map_ix_jx: dict[tuple[int, int], list[int]] = {}
-    n_tok = len(token_boundaries)
-
-    for ix_word in words_boundaries:
-        wa, wb = ix_word
-        hits: list[int] = []
-        for j in range(n_tok):
-            ta = int(token_boundaries[j][0])
-            tb = int(token_boundaries[j][1])
-            if tb <= ta:
-                continue
-            if ta < wb and tb > wa:
-                hits.append(j)
-        map_ix_jx[ix_word] = hits
-
-    return map_ix_jx
-
-
-def text_to_tokens(nlp, text) -> list[SimplifiedToken]:
-    stokens = [
-        SimplifiedToken(
-            **{
-                "lemma": token.lemma_,
-                "text": token.text,
-                "tag": token.tag_,
-                "pos": token.pos_,
-                "is_stop": bool(token.is_stop),
-                "ix": token.idx,
-                "ix_end": token.idx + len(token),
-            }
-        )
-        for token in nlp(text)
-    ]
-
-    return stokens
-
-
-def keep_expression_for_prediction(expr: Expression) -> bool:
-    """Whether to keep a sliding-window mention for :meth:`~pelinker.model.Linker.predict`.
-
-    Drops any window that contains punctuation (spaCy ``pos_ == "PUNCT"``). Drops
-    windows whose tokens are **all** stop words; keeps windows that mix content and
-    function words (e.g. ``type of``).
-    """
-    toks = expr.tokens
-    if not toks:
-        return False
-    if any(t.pos == "PUNCT" for t in toks if t.pos is not None):
-        return False
-    if all(t.is_stop is True for t in toks):
-        return False
-    return True
-
-
 def extract_ordered_mention_tensors(
     report_batch: ReportBatch,
     *,
@@ -301,100 +137,6 @@ def extract_ordered_mention_tensors(
     return tt_list
 
 
-def split_into_sentences(text):
-    text = re.sub(r"\s+", " ", text)
-
-    # split on .!? if followed by a capital and not preceded by a capital
-    pat = r"(?<=[^A-Z][.!?])\s*(?=[A-Z])"
-    phrases_ = re.split(pat, text)
-    # trim initial/terminal whitespaces
-    trim_whitespace = re.compile(r"^[\s+]+|[\s+]+$")
-    phrases_ = [trim_whitespace.sub("", p) for p in phrases_]
-    return phrases_
-
-
-def process_text(
-    batched_texts: list[list[str]],
-    tokenizer,
-    model,
-    *,
-    keep_hidden_states_on_device: bool = False,
-) -> ChunkMapper:
-    """Encode all text chunks in one forward pass and build a :class:`~pelinker.onto.ChunkMapper`.
-
-    Flattens ``batched_texts`` (each inner list is one logical document split into
-    length-limited chunks), runs :func:`text_to_tokens_embeddings` once, and packages
-    tensors with bookkeeping to map chunk indices back to ``(document_index,
-    chunk_index_within_document)`` and cumulative character offsets within each document.
-
-    Args:
-        batched_texts: ``batched_texts[i]`` is the list of chunk strings for document ``i``.
-        tokenizer: Hugging Face tokenizer for ``model``.
-        model: Hugging Face model used with hidden states.
-        keep_hidden_states_on_device: If True, leave activations on the model device
-            (see :func:`text_to_tokens_embeddings`).
-
-    Returns:
-        A :class:`~pelinker.onto.ChunkMapper` whose ``tensor`` axis ``1`` runs over all
-        chunks in flatten order (same order as ``chunks``).
-    """
-
-    flattened_chunks: list[str] = [s for group in batched_texts for s in group]
-
-    it_ic = [
-        (i_t, i_c) for i_t, sl in enumerate(batched_texts) for i_c, _ in enumerate(sl)
-    ]
-
-    chunk_cumlens = [
-        reduce(lambda a, x: a + [a[-1] + x], [len(item) for item in chunks], [0])
-        for chunks in batched_texts
-    ]
-
-    tt, offsets = text_to_tokens_embeddings(
-        flattened_chunks,
-        tokenizer,
-        model,
-        keep_hidden_states_on_device=keep_hidden_states_on_device,
-    )
-
-    return ChunkMapper(
-        tensor=tt,
-        chunks=flattened_chunks,
-        token_spans_list=offsets,
-        it_ic=it_ic,
-        cumulative_lens=chunk_cumlens,
-    )
-
-
-def map_words_to_tokens(
-    text_token_spans: list[tuple[int, int]], text_word_spans: list[tuple[int, int]]
-) -> tuple[list[tuple[int, int]], list[tuple[int, int]]]:
-    """
-    given text token and word spans,
-
-        words : [...(start_pos_i, end_pos_i)...]
-        tokens : [...(start_pos_i, end_pos_i)...]
-
-    we define work -> token spans, i.e. a mapping of which tokens belong to words groups
-
-    if there is a positive overlap between word i and token j spans,
-        we consider that token j belongs to work i group
-
-    return a list of work -> token bounds : [...(start_token_i, end_token_i)...]
-    and a refreshed
-
-
-    """
-
-    map_ix_jx = map_spans_to_spans_basic(text_word_spans, text_token_spans)
-
-    # sanitize token offsets
-    map_ix_jx = {k: v for k, v in map_ix_jx.items() if v}
-    text_word_spans = [x for x in map_ix_jx.keys()]
-    token_word_spans = [(y[0], y[-1] + 1) for y in map_ix_jx.values()]
-    return text_word_spans, token_word_spans
-
-
 def tt_aggregate_normalize(tt: torch.Tensor, ls):
     """
 
@@ -427,8 +169,8 @@ def tt_normalize(cm: ChunkMapper, layers) -> list[list[torch.Tensor]]:
 
     Args:
         cm: Chunk mapper with ``token_word_spans_list`` set by
-            :meth:`~pelinker.onto.ChunkMapper.set_token_word_spans` /
-            :meth:`~pelinker.onto.ChunkMapper.set_mapping_table`.
+            :meth:`~pelinker.core.onto.ChunkMapper.set_token_word_spans` /
+            :meth:`~pelinker.core.onto.ChunkMapper.set_mapping_table`.
         layers: Layer indices (typically negative indices into ``hidden_states``, e.g.
             ``[-1]`` or ``[-2, -1]``).
 
@@ -448,164 +190,6 @@ def tt_normalize(cm: ChunkMapper, layers) -> list[list[torch.Tensor]]:
     return tt_r
 
 
-def compute_distance_ref(  # used by run/obsolete only
-    index,
-    tt_descs,
-    nb_nn,
-    gt_position,
-):
-    distance_matrix, nearest_neighbors_matrix = index.search(tt_descs, nb_nn)
-    dfd = pd.DataFrame(distance_matrix[:, 0], columns=["dist"])
-    dfd["position"] = "top"
-    dfd2 = pd.DataFrame(distance_matrix[:, 1:].flatten(), columns=["dist"])
-    dfd2["position"] = "1-9"
-    dfa = pd.concat([dfd, dfd2])
-    acc_score = accuracy_score(
-        nearest_neighbors_matrix[:, 0],
-        gt_position,
-    )
-
-    top_mean = distance_matrix[:, 0].mean()
-    below_mean = distance_matrix[:, 1:].flatten().mean()
-
-    top_cand_dist = top_mean - below_mean
-    m0 = (top_cand_dist, acc_score)
-    return m0, dfa
-
-
-def encode(texts, tokenizer, model, ls):  # used by run/obsolete only
-    if ls == "sent":
-        tt_labels = model.encode(texts, normalize_embeddings=True)
-    else:
-        tt_labels_layered, labels_spans = text_to_tokens_embeddings(
-            texts, tokenizer, model
-        )
-        layers = normalize_layers_spec(ls, n_hidden_states=tt_labels_layered.shape[0])
-        tt_labels = tt_aggregate_normalize(tt_labels_layered, layers)
-    return tt_labels
-
-
-def fetch_latest_kb(
-    path_derived,
-) -> tuple[str | None, int]:  # used by run/obsolete only
-    file_names = [
-        file.name
-        for file in path_derived.iterdir()
-        if file.is_file() and ".synthesis." in file.name
-    ]
-    filename_versions = sorted(
-        [(f, int(f.split(".")[-2])) for f in file_names], key=lambda x: x[1]
-    )
-    if filename_versions:
-        return filename_versions[-1]
-    else:
-        return None, -1
-
-
-def split_text_into_batches(text: str, max_length: int) -> list[str]:
-    """Split a single string into chunks no longer than ``max_length`` characters.
-
-    Uses a regex that prefers breaking after whitespace near the limit; a chunk may
-    reach ``max_length`` when no earlier break exists.
-
-    Args:
-        text: Full document or segment to split.
-        max_length: Maximum characters per chunk (typically tokenizer/model limit).
-
-    Returns:
-        Non-empty string segments whose concatenation recovers ``text`` (aside from
-        regex edge cases on pathological input).
-    """
-    pattern = (
-        r"(.{1," + str(max_length - 1) + r"})(\s|$)|(.{1," + str(max_length) + r"})"
-    )
-
-    matches = re.findall(pattern, text)
-    batched = ["".join(parts) for parts in matches]
-    return batched
-
-
-def split_text_into_token_budget(text: str, tokenizer, max_tokens: int) -> list[str]:
-    """Split *text* so each segment encodes to at most *max_tokens* subword tokens.
-
-    Uses a longest-prefix binary search per segment (by character offset), then
-    prefers breaking at the last space still within the token budget. Avoids relying
-    on a fixed character cap, which can exceed the model's tokenizer limit.
-
-    Args:
-        text: Full document string for one logical chunking pass.
-        tokenizer: Hugging Face tokenizer (``encode(..., add_special_tokens=False)``).
-        max_tokens: Maximum subword count per segment (typically ``MAX_LENGTH``).
-
-    Returns:
-        Segments whose concatenation equals *text* exactly (no dropped characters).
-
-    Raises:
-        ValueError: If ``max_tokens < 1``, or a minimal slice still exceeds the budget.
-    """
-    if max_tokens < 1:
-        raise ValueError("max_tokens must be at least 1")
-    if not text:
-        return []
-
-    def n_tokens(segment: str) -> int:
-        return len(tokenizer.encode(segment, add_special_tokens=False))
-
-    chunks: list[str] = []
-    start = 0
-    n = len(text)
-    while start < n:
-        if n_tokens(text[start:n]) <= max_tokens:
-            chunks.append(text[start:n])
-            break
-
-        lo, hi = start + 1, n + 1
-        best = start
-        while lo < hi:
-            mid = (lo + hi) // 2
-            if n_tokens(text[start:mid]) <= max_tokens:
-                best = mid
-                lo = mid + 1
-            else:
-                hi = mid
-        if best <= start:
-            raise ValueError(
-                "a minimal text slice exceeds max_tokens; increase max_length or inspect the tokenizer"
-            )
-
-        end = best
-        sp = text.rfind(" ", start + 1, end)
-        if sp > start and n_tokens(text[start:sp]) <= max_tokens:
-            end = sp
-
-        chunk = text[start:end]
-        chunks.append(chunk)
-        start = end
-
-    return chunks
-
-
-def get_word_boundaries(text) -> list[tuple[int, int]]:
-    """
-        render word boundaries in text
-
-    :param text:
-    :return:
-    """
-
-    ix_whitespaces = [0] + [
-        i + 1
-        for i, char in enumerate(text)
-        if char in whitespace or char in punctuation
-    ]
-    if text[-1] not in whitespace and text[-1] not in punctuation:
-        ix_whitespaces += [len(text) + 1]
-
-    ix_words = [(i, j - 1) for i, j in zip(ix_whitespaces, ix_whitespaces[1:])]
-    ix_words = [(i, j) for i, j in ix_words if i != j]
-    return ix_words
-
-
 def render_elementary_tensor_table(
     chunk_mapper: ChunkMapper,
     text_word_spans: list[list[tuple[int, int]]],
@@ -637,38 +221,6 @@ def render_elementary_tensor_table(
     ]
 
 
-def build_expression_container(
-    cm: ChunkMapper,
-    expression_lists_per_chunk: list[list[Expression]],
-    word_grouping: WordGrouping,
-) -> ExpressionHolderBatch:
-    """Merge per-chunk expressions and embedding rows into one holder per document.
-
-    For each document index, concatenates embedding tensors for all its chunks (in
-    chunk order) and concatenates expression lists the same way, so downstream code
-    can match lemmas and spans at document level.
-
-    Args:
-        cm: Chunk mapper with ``tt_expressions`` and ``text_chunk_map`` populated.
-        expression_lists_per_chunk: Parallel to ``cm.chunks``: expressions for each
-            chunk (already filtered to align with ``tt_expressions`` rows where needed).
-        word_grouping: Which :class:`~pelinker.onto.WordGrouping` this batch describes.
-
-    Returns:
-        An :class:`~pelinker.onto.ExpressionHolderBatch` with one
-        :class:`~pelinker.onto.ExpressionHolder` per input document.
-    """
-    texts = []
-    for itext, ichunks in cm.text_chunk_map.items():
-        expressions = reduce(
-            lambda a, b: a + b, [expression_lists_per_chunk[i] for i in ichunks]
-        )
-        tt_merged = torch.cat([cm.tt_expressions[i] for i in ichunks])
-        texts.append(ExpressionHolder(tt=tt_merged, expressions=expressions))
-
-    return ExpressionHolderBatch(expression_data=texts, word_grouping=word_grouping)
-
-
 def texts_to_vrep(
     texts: list[str],
     tokenizer,
@@ -688,7 +240,7 @@ def texts_to_vrep(
     1. Split each document into chunks (token-budget by default, see ``chunk_by_token_budget``).
     2. Encode all chunks in one transformer forward pass (:func:`process_text`).
     3. For each chunk, tokenize with spaCy once (:func:`text_to_tokens`) and build sliding
-       windows of ``w`` tokens per :class:`~pelinker.onto.WordGrouping`
+       windows of ``w`` tokens per :class:`~pelinker.core.onto.WordGrouping`
        (:func:`token_list_with_window`).
     4. Map word character spans to tokenizer token ranges and pool layer activations
        (:func:`render_elementary_tensor_table` → :func:`tt_normalize`).
@@ -699,7 +251,7 @@ def texts_to_vrep(
     spaCy windows and pooling targets differ. Each pass over ``word_modes`` calls
     :func:`render_elementary_tensor_table`, so fields on ``chunk_mapper`` such as
     ``tt_expressions`` and ``text_word_spans_list`` reflect **only the last** grouping;
-    use the :class:`~pelinker.onto.ReportBatch` slots for per-mode tensors.
+    use the :class:`~pelinker.core.onto.ReportBatch` slots for per-mode tensors.
 
     Args:
         texts: One string per document (logical item in the returned batch).
@@ -708,7 +260,7 @@ def texts_to_vrep(
         layers_spec: Layer selection; string digits (``\"12\"``) or negative indices; see
             :func:`normalize_layers_spec`.
         word_modes: For each mode, build ``W1``/``W2``/… token windows and a separate
-            :class:`~pelinker.onto.ExpressionHolderBatch` in the result.
+            :class:`~pelinker.core.onto.ExpressionHolderBatch` in the result.
         nlp: Loaded spaCy pipeline (:func:`text_to_tokens`).
         max_length: When ``chunk_by_token_budget`` is True, max **subword tokens** per
             chunk; when False, max **characters** (legacy :func:`split_text_into_batches`).
@@ -717,8 +269,8 @@ def texts_to_vrep(
             device (saves host RAM; requires GPU memory for large batches).
 
     Returns:
-        :class:`~pelinker.onto.ReportBatch` containing the shared
-        :class:`~pelinker.onto.ChunkMapper` and, for each ``word_grouping``, document-level
+        :class:`~pelinker.core.onto.ReportBatch` containing the shared
+        :class:`~pelinker.core.onto.ChunkMapper` and, for each ``word_grouping``, document-level
         expression holders with pooled embeddings.
     """
     if chunk_by_token_budget:
@@ -744,14 +296,14 @@ def texts_to_vrep(
     )
 
     # spaCy once per encoder chunk (reused for every word_modes pass)
-    stoken_per_chunk: list[list[SimplifiedToken]] = [
-        text_to_tokens(nlp=nlp, text=chunk) for chunk in chunk_mapper.chunks
+    stokens_per_chunk: list[list[SimplifiedToken]] = [
+        tokens_from_doc(doc) for doc in nlp.pipe(chunk_mapper.chunks)
     ]
 
     # ichunk -> itext, ichunk local
     ichunk_to_itext_ichunk_local_list = [
         chunk_mapper.ichunk_to_itext_ichunk_local(k)
-        for k, _ in enumerate(stoken_per_chunk)
+        for k, _ in enumerate(stokens_per_chunk)
     ]
 
     data: list[ExpressionHolderBatch] = []
@@ -761,7 +313,7 @@ def texts_to_vrep(
                 chunk_tokens, word_grouping, ichunk=ichunk_local, itext=itext
             )
             for chunk_tokens, (itext, ichunk_local) in zip(
-                stoken_per_chunk, ichunk_to_itext_ichunk_local_list
+                stokens_per_chunk, ichunk_to_itext_ichunk_local_list
             )
         ]
 
@@ -795,60 +347,12 @@ def texts_to_vrep(
                 word_grouping=word_grouping,
             )
         ]
-    return ReportBatch(chunk_mapper=chunk_mapper, texts=texts, _data=data)
-
-
-def token_list_with_window(
-    tokens: list[SimplifiedToken], window: WordGrouping, itext=None, ichunk=None
-) -> list[Expression]:
-    """Build every contiguous ``window``-token slice as an :class:`~pelinker.onto.Expression`.
-
-    Each expression stores the participating :class:`~pelinker.onto.SimplifiedToken`
-    objects and, after ``__post_init__``, character bounds ``a``/``b`` for the span.
-
-    Args:
-        tokens: spaCy-derived tokens for one chunk.
-        window: Window size via :class:`~pelinker.onto.WordGrouping` (``W1`` → 1 token, etc.).
-        itext: Document index in the outer batch (optional metadata on expressions).
-        ichunk: Chunk index within the document (optional metadata).
-
-    Returns:
-        Length ``len(tokens) - w + 1`` list of expressions (empty if ``len(tokens) < w``).
-    """
-    agg = []
-    w = int(window.value)
-    for k in range(len(tokens) - w + 1):
-        agg.append(Expression(tokens=tokens[k : k + w], itext=itext, ichunk=ichunk))
-    return agg
-
-
-def map_words_to_tokens_list(
-    text_token_spans_list: list[list[tuple[int, int]]],
-    text_word_spans_list: list[list[tuple[int, int]]],
-):
-    """
-    take a batch of token spans and a batch of word spans
-
-    return a batch of work to token maps
-        and also an updated batch of word spans
-            (some words can not be mapped to tokens, so they are excluded)
-
-
-    """
-
-    text_word_spans_list_: list[list[tuple[int, int]]] = []
-    token_work_spans_list: list[list[tuple[int, int]]] = []
-
-    for text_token_spans, text_word_spans in zip(
-        text_token_spans_list, text_word_spans_list
-    ):
-        text_word_spans, token_word_spans = map_words_to_tokens(
-            text_token_spans, text_word_spans
-        )
-        text_word_spans_list_ += [text_word_spans]
-        token_work_spans_list += [token_word_spans]
-
-    return token_work_spans_list, text_word_spans_list_
+    return ReportBatch(
+        chunk_mapper=chunk_mapper,
+        texts=texts,
+        _data=data,
+        stokens_per_chunk=stokens_per_chunk,
+    )
 
 
 def embed_texts(
@@ -871,7 +375,7 @@ def embed_texts(
     Returns:
         List of embedding tensors, one per phrase
     """
-    from pelinker.onto import WordGrouping
+    from pelinker.core.onto import WordGrouping
 
     phrases_list = [str(p).strip() for p in phrases if pd.notna(p) and str(p).strip()]
 
@@ -894,15 +398,6 @@ def embed_texts(
     return text_embeddings
 
 
-def _available_word_groupings(
-    report_batch: ReportBatch,
-) -> tuple[frozenset[WordGrouping], tuple[WordGrouping, ...]]:
-    """Membership set plus deterministic iteration order for negative-candidate walks."""
-    members = frozenset(report_batch.available_groupings())
-    ordered = tuple(sorted(members, key=lambda wg: wg.value))
-    return members, ordered
-
-
 def _sample_row_indices(rng: np.random.RandomState, n_pool: int, k: int) -> np.ndarray:
     """Sample ``k`` indices in ``[0, n_pool)``; with replacement only when ``k > n_pool``."""
     if k <= n_pool:
@@ -932,6 +427,8 @@ def _mention_row_dict(
     itext: int,
     ichunk: int,
     embed_list: list[float],
+    direction: str | None = None,
+    surface_rule: str | None = None,
 ) -> dict[str, object]:
     return {
         "pmid": pmid,
@@ -944,9 +441,91 @@ def _mention_row_dict(
         "itext": itext,
         "ichunk": ichunk,
         "embed": embed_list,
+        "direction": direction,
+        "surface_rule": surface_rule,
     }
 
 
+def split_predicate_labels(
+    entities: list[str],
+    nlp,
+    symmetric_labels: frozenset[str] = frozenset(),
+) -> tuple[list[PredicateSpec], list[str]]:
+    """Verb-predicate labels (matched from the parse) and the rest (matched lexically)."""
+    return _split_predicate_labels(
+        entities, nlp, symmetric_labels, tokenize=text_to_tokens
+    )
+
+
+def one_label_per_span(rows: list[dict]) -> list[dict]:
+    """Keep one mention row per site (:func:`pelinker.text.mentions.keep_one_per_site`)."""
+    return keep_one_per_site(
+        rows,
+        span=lambda r: (int(r["a_abs"]), int(r["b_abs"])),
+        preference=lambda r: (
+            1 if r.get("surface_rule") == LEXICAL_RULE else 0,
+            str(r["entity"]),
+        ),
+    )
+
+
+def _predicate_rows(
+    report_batch: "ReportBatch",
+    matcher: PredicateMatcher,
+    containers_by_wg: dict[WordGrouping, ExpressionHolderBatch],
+    pmids: list[str],
+) -> list[list[dict]]:
+    """Parse-labelled mention rows per text; embeddings come from the W1/W2 windows."""
+    out: list[list[dict]] = [[] for _ in report_batch.texts]
+    stokens = report_batch.stokens_per_chunk
+    if stokens is None:
+        return out
+    lookup: dict[tuple[WordGrouping, int], dict[tuple[int, int, int], int]] = {}
+
+    def index_for(wg: WordGrouping, itext: int) -> dict[tuple[int, int, int], int]:
+        key = (wg, itext)
+        if key not in lookup:
+            holder = containers_by_wg[wg].expression_data[itext]
+            lookup[key] = {
+                (int(e.ichunk or 0), int(e.a or 0), int(e.b or 0)): k
+                for k, e in enumerate(holder.expressions)
+            }
+        return lookup[key]
+
+    cm = report_batch.chunk_mapper
+    for k, tokens in enumerate(stokens):
+        itext, ichunk_local = cm.ichunk_to_itext_ichunk_local(k)
+        by_i = {t.i: t for t in tokens}
+        for m in matcher.match(tokens):
+            wg = WordGrouping.W1 if m.start == m.head else WordGrouping.W2
+            if wg not in containers_by_wg:
+                continue
+            a, b = by_i[m.start].ix, by_i[m.head].ix_end
+            pos = index_for(wg, itext).get((ichunk_local, a, b))
+            if pos is None:  # window lost in tokenizer alignment
+                continue
+            holder = containers_by_wg[wg].expression_data[itext]
+            offset = cm.map_chunk_to_text(itext, ichunk_local)
+            text = report_batch.texts[itext]
+            out[itext].append(
+                _mention_row_dict(
+                    pmid=pmids[itext],
+                    entity=m.label,
+                    mention=text[offset + a : offset + b],
+                    a=a,
+                    b=b,
+                    a_abs=offset + a,
+                    b_abs=offset + b,
+                    itext=itext,
+                    ichunk=ichunk_local,
+                    embed_list=holder.tt[pos].numpy().tolist(),
+                    direction=m.direction,
+                    surface_rule=m.rule,
+                )
+            )
+    return out
+
+
 def extract_and_embed_mentions(
     entities: list[str],
     data: list[str],
@@ -962,11 +541,19 @@ def extract_and_embed_mentions(
     random_seed: int | None = None,
     negative_random_state: np.random.RandomState | None = None,
     on_encoder_batch: Callable[[int, int, int], None] | None = None,
+    symmetric_labels: frozenset[str] = frozenset(),
 ) -> List[dict]:
     """
     Modified to return list of dicts instead of DataFrame for better memory management
     and consistent schema handling.
 
+    Labels that are verb predicates are matched from the dependency parse
+    (:mod:`pelinker.text.predicates`): each verb mention gets at most one label, chosen
+    by voice, with ``direction`` and ``surface_rule`` recorded on the row. Other labels
+    are matched lexically on lemma windows (``surface_rule="lexical"``). Either way a
+    mention site keeps one label (:func:`one_label_per_span`). ``symmetric_labels`` are
+    never given an inverse direction.
+
     Negative rows are sampled with :class:`numpy.random.RandomState`. Pass
     ``negative_random_state`` to reuse one RNG across several calls (e.g. successive
     read buffers); otherwise ``random_seed`` builds a fresh ``RandomState`` per call.
@@ -994,9 +581,14 @@ def extract_and_embed_mentions(
         data_pmids[i : i + batch_size] for i in range(0, len(data), batch_size)
     ]
 
+    predicate_specs, lexical_entities = split_predicate_labels(
+        entities, nlp, symmetric_labels
+    )
+    matcher = PredicateMatcher(predicate_specs)
+
     # Pre-tokenize entities and resolve each entity's matching grouping once.
     entity_specs: list[tuple[str, list[SimplifiedToken], WordGrouping]] = []
-    for entity in entities:
+    for entity in lexical_entities:
         wg = _wg_for_property(entity)
         if wg is None:
             continue
@@ -1049,9 +641,30 @@ def extract_and_embed_mentions(
                             itext=e.itext,
                             ichunk=e.ichunk,
                             embed_list=tt.numpy().tolist(),
+                            surface_rule=LEXICAL_RULE,
                         )
                     )
 
+        predicate_rows = _predicate_rows(
+            report_batch, matcher, containers_by_wg, batch_pmids
+        )
+        for itext in range(len(report_batch.texts)):
+            merged = one_label_per_span(
+                positive_rows_by_text[itext] + predicate_rows[itext]
+            )
+            positive_rows_by_text[itext] = merged
+            positive_keys_by_text[itext] = {
+                _mention_key(
+                    wg.value,
+                    int(r["ichunk"]),
+                    int(r["a"]),
+                    int(r["b"]),
+                    str(r["mention"]),
+                )
+                for r in merged
+                for wg in wg_iter_order
+            }
+
         # Phase B: emit positives and optionally add globally-negative samples.
         if negatives_per_positive == 0:
             for text_rows in positive_rows_by_text:
@@ -1112,3 +725,181 @@ def extract_and_embed_mentions(
             on_encoder_batch(ibatch, n_batches, len(rows))
 
     return rows
+
+
+def process_text(
+    batched_texts: list[list[str]],
+    tokenizer,
+    model,
+    *,
+    keep_hidden_states_on_device: bool = False,
+) -> ChunkMapper:
+    """Encode all text chunks in one forward pass and build a :class:`~pelinker.core.onto.ChunkMapper`.
+
+    Flattens ``batched_texts`` (each inner list is one logical document split into
+    length-limited chunks), runs :func:`text_to_tokens_embeddings` once, and packages
+    tensors with bookkeeping to map chunk indices back to ``(document_index,
+    chunk_index_within_document)`` and cumulative character offsets within each document.
+
+    Args:
+        batched_texts: ``batched_texts[i]`` is the list of chunk strings for document ``i``.
+        tokenizer: Hugging Face tokenizer for ``model``.
+        model: Hugging Face model used with hidden states.
+        keep_hidden_states_on_device: If True, leave activations on the model device
+            (see :func:`text_to_tokens_embeddings`).
+
+    Returns:
+        A :class:`~pelinker.core.onto.ChunkMapper` whose ``tensor`` axis ``1`` runs over all
+        chunks in flatten order (same order as ``chunks``).
+    """
+
+    flattened_chunks: list[str] = [s for group in batched_texts for s in group]
+
+    it_ic = [
+        (i_t, i_c) for i_t, sl in enumerate(batched_texts) for i_c, _ in enumerate(sl)
+    ]
+
+    chunk_cumlens = [
+        reduce(lambda a, x: a + [a[-1] + x], [len(item) for item in chunks], [0])
+        for chunks in batched_texts
+    ]
+
+    tt, offsets = text_to_tokens_embeddings(
+        flattened_chunks,
+        tokenizer,
+        model,
+        keep_hidden_states_on_device=keep_hidden_states_on_device,
+    )
+
+    return ChunkMapper(
+        tensor=tt,
+        chunks=flattened_chunks,
+        token_spans_list=offsets,
+        it_ic=it_ic,
+        cumulative_lens=chunk_cumlens,
+    )
+
+
+@dataclasses.dataclass
+class ReportBatch(BaseDataclass):
+    """Batch of texts with shared encoder state and one holder list per ``WordGrouping``.
+
+    ``chunk_mapper`` is shared across groupings; its span/pooling fields may match
+    only the **last** mode processed—use ``_data`` / ``__getitem__`` for mode-specific
+    embeddings and expressions.
+    """
+
+    _data: list[ExpressionHolderBatch]
+    texts: list[str]
+    chunk_mapper: ChunkMapper
+    # Parsed tokens per encoder chunk (global chunk order), for parse-based labelling.
+    stokens_per_chunk: list[list[SimplifiedToken]] | None = None
+
+    def __post_init__(self):
+        for item in self._data:
+            if len(self.texts) != len(item.expression_data):
+                raise ValueError(
+                    "The number of ExpressionHolders does not match the number of texts"
+                )
+
+    def __getitem__(self, wg: WordGrouping) -> ExpressionHolderBatch:
+        filtered = [item for item in self._data if item.word_grouping == wg]
+        if filtered:
+            return filtered[0]
+        else:
+            raise ValueError(f"{wg} not available")
+
+    def get_data_for_grouping(self, wg: WordGrouping):
+        return [item for item in self._data if item.word_grouping == wg]
+
+    def available_groupings(self):
+        return [item.word_grouping for item in self._data]
+
+    def get_text_embeddings(self, layers_spec: str | list[int]):
+        """
+        Extract sentence-level embeddings for each text in the batch.
+
+        Args:
+            layers_spec: Layer specification (string digits or negative indices); see
+                :func:`pelinker.text.models.normalize_layers_spec`. Not ``\"sent\"``.
+
+        Returns:
+            list[torch.Tensor]: List of embeddings, one per text in self.texts
+        """
+
+        layers = normalize_layers_spec(
+            layers_spec,
+            n_hidden_states=self.chunk_mapper.tensor.shape[0],
+        )
+        # Extract chunk-level embeddings from chunk_mapper.tensor
+        # chunk_mapper.tensor has shape: n_layers x n_chunks x n_tokens x n_emb
+        chunk_embeddings = tt_aggregate_normalize(self.chunk_mapper.tensor, layers)
+
+        # Map chunks back to texts
+        # Group chunks by text index
+        text_chunks = {}
+        for ichunk, (itext, ichunk_local) in enumerate(self.chunk_mapper.it_ic):
+            if itext not in text_chunks:
+                text_chunks[itext] = []
+            text_chunks[itext].append(ichunk)
+
+        # Aggregate embeddings for each text (average if multiple chunks)
+        text_embeddings = []
+        for itext in range(len(self.texts)):
+            chunk_indices = text_chunks.get(itext, [])
+            if chunk_indices:
+                # Get embeddings for all chunks of this text
+                chunk_embs = chunk_embeddings[chunk_indices]
+                # Average over chunks if multiple chunks
+                if len(chunk_indices) > 1:
+                    text_emb = chunk_embs.mean(dim=0)
+                else:
+                    text_emb = chunk_embs[0]
+                text_embeddings.append(text_emb)
+            else:
+                # Fallback: create zero embedding if no chunks found
+                # This shouldn't happen, but handle it gracefully
+                text_embeddings.append(torch.zeros_like(chunk_embeddings[0]))
+
+        return text_embeddings
+
+
+def build_expression_container(
+    cm: ChunkMapper,
+    expression_lists_per_chunk: list[list[Expression]],
+    word_grouping: WordGrouping,
+) -> ExpressionHolderBatch:
+    """Merge per-chunk expressions and embedding rows into one holder per document.
+
+    For each document index, concatenates embedding tensors for all its chunks (in
+    chunk order) and concatenates expression lists the same way, so downstream code
+    can match lemmas and spans at document level.
+
+    Args:
+        cm: Chunk mapper with ``tt_expressions`` and ``text_chunk_map`` populated.
+        expression_lists_per_chunk: Parallel to ``cm.chunks``: expressions for each
+            chunk (already filtered to align with ``tt_expressions`` rows where needed).
+        word_grouping: Which :class:`~pelinker.core.onto.WordGrouping` this batch describes.
+
+    Returns:
+        An :class:`~pelinker.core.onto.ExpressionHolderBatch` with one
+        :class:`~pelinker.core.onto.ExpressionHolder` per input document.
+    """
+    texts = []
+    for itext, ichunks in cm.text_chunk_map.items():
+        expressions = reduce(
+            lambda a, b: a + b, [expression_lists_per_chunk[i] for i in ichunks]
+        )
+        tt_merged = torch.cat([cm.tt_expressions[i] for i in ichunks])
+        texts.append(ExpressionHolder(tt=tt_merged, expressions=expressions))
+
+    return ExpressionHolderBatch(expression_data=texts, word_grouping=word_grouping)
+
+
+def _available_word_groupings(
+    report_batch: ReportBatch,
+) -> tuple[frozenset[WordGrouping], tuple[WordGrouping, ...]]:
+    """Membership set plus deterministic iteration order for negative-candidate walks."""
+    members = frozenset(report_batch.available_groupings())
+    ordered = tuple(sorted(members, key=lambda wg: wg.value))
+    return members, ordered
diff --git a/pelinker/text/lexical.py b/pelinker/text/lexical.py
new file mode 100644
index 0000000..a1439a3
--- /dev/null
+++ b/pelinker/text/lexical.py
@@ -0,0 +1,228 @@
+from __future__ import annotations
+
+from collections.abc import Iterable, Mapping
+
+import numpy as np
+
+
+"""Lexical scoring: corpus word frequencies, label simplicity, KB generality."""
+
+
+def get_word_frequencies_from_library(
+    language: str = "en",
+    wordlist: str = "best",
+) -> object | None:
+    """
+    Get word frequency lookup object from wordfreq library.
+
+    Args:
+        language: Language code (default: "en" for English)
+        wordlist: Wordlist size - "best", "large", or "small" (default: "best")
+
+    Returns:
+        WordFrequencyLookup object with .get() method, or None if library not available
+    """
+    try:
+        from wordfreq import word_frequency  # type: ignore
+
+        # Return a callable that looks up frequencies
+        # We'll use a lazy lookup approach
+        class WordFrequencyLookup:
+            def __init__(self, lang: str, wlist: str):
+                self.lang = lang
+                self.wlist = wlist
+                self._cache: dict[str, float] = {}
+
+            def get(self, word: str, default: float = 0.0) -> float:
+                word_lower = word.lower()
+                if word_lower not in self._cache:
+                    try:
+                        self._cache[word_lower] = word_frequency(
+                            word_lower, self.lang, wordlist=self.wlist
+                        )
+                    except (KeyError, ValueError):
+                        self._cache[word_lower] = default
+                return self._cache[word_lower]
+
+            def __getitem__(self, word: str) -> float:
+                return self.get(word)
+
+        return WordFrequencyLookup(language, wordlist)  # type: ignore
+    except ImportError:
+        return None
+
+
+def _measure_label_simplicity(
+    label: str,
+    word_frequencies: Mapping[str, float],
+    stopwords: Iterable[str] = (
+        "is",
+        "of",
+        "the",
+        "a",
+        "an",
+        "to",
+        "for",
+        "or",
+        "in",
+        "has",
+    ),
+    zero_freq_penalty: float = 1e-8,
+    multiword_penalty: float = 0.2,
+    stopword_penalty: float = 0.3,
+) -> dict[str, int | float]:
+    """..."""
+
+    text = label.strip().lower()
+
+    # Handle empty labels
+    if not text:
+        return {"char_count": 0, "word_count": 0, "simplicity_score": 0.0}
+
+    words = text.split()
+    word_count = len(words)
+
+    stopword_set = set(stopwords)
+    content_words = [w for w in words if w not in stopword_set]
+    stopword_count = word_count - len(content_words)
+
+    # Handle labels with only stopwords
+    if not content_words:
+        return {
+            "char_count": len(text),
+            "word_count": word_count,
+            "simplicity_score": zero_freq_penalty,
+        }
+
+    # Get frequencies for content words
+    content_freqs = [word_frequencies.get(w, zero_freq_penalty) for w in content_words]
+
+    # Harmonic mean
+    harmonic_mean_freq = len(content_freqs) / sum(
+        1.0 / max(f, zero_freq_penalty) for f in content_freqs
+    )
+
+    # Apply penalties multiplicatively (but ensure they don't go negative)
+    penalty_factor = 1.0
+
+    if word_count > 1:
+        penalty_factor *= max(0.0, 1.0 - multiword_penalty * (word_count - 1))
+
+    if stopword_count > 0 and word_count > 1:
+        penalty_factor *= max(0.0, 1.0 - stopword_penalty * stopword_count)
+
+    simplicity_score = harmonic_mean_freq * penalty_factor
+
+    return {
+        "char_count": len(text),
+        "word_count": word_count,
+        "simplicity_score": simplicity_score,
+    }
+
+
+def compute_kb_generality_scores(
+    embeddings: np.ndarray,
+    labels: list[str],
+    k_neighbors: int = 10,
+    metric: str = "cosine",
+    word_frequencies: Mapping[str, float] | None = None,
+    density_weight: float = 0.5,
+) -> np.ndarray:
+    """
+    Compute generality scores for entities based on KB statistics.
+
+    Combines embedding-space density with label simplicity to identify generic vs specific terms.
+    Generic terms tend to have:
+    - Many similar neighbors (high density)
+    - High average similarity to neighbors
+    - Shorter, simpler labels (fewer words, common words)
+    - Central position in semantic space
+
+    Args:
+        embeddings: Array of shape (n_points, n_features) containing embeddings
+        labels: List of labels corresponding to embeddings
+        k_neighbors: Number of nearest neighbors to consider
+        metric: Distance metric ('cosine' or 'euclidean')
+        word_frequencies: Optional word frequency mapping for simplicity scoring
+        density_weight: Weight for embedding density vs label simplicity (0.0 = pure simplicity, 1.0 = pure density)
+
+    Returns:
+        Array of generality scores (higher = more generic), shape (n_points,)
+    """
+    from sklearn.neighbors import NearestNeighbors
+
+    n_points = embeddings.shape[0]
+    k_neighbors = min(k_neighbors, n_points - 1)
+
+    if k_neighbors < 1:
+        return np.ones(n_points)
+
+    # Normalize embeddings for cosine distance
+    if metric == "cosine":
+        embeddings_norm = embeddings / (
+            np.linalg.norm(embeddings, axis=1, keepdims=True) + 1e-8
+        )
+    else:
+        embeddings_norm = embeddings
+
+    # Find k nearest neighbors for each point
+    nn = NearestNeighbors(n_neighbors=k_neighbors + 1, metric=metric)
+    nn.fit(embeddings_norm)
+    distances, indices = nn.kneighbors(embeddings_norm)
+
+    # Compute embedding-space density scores
+    density_scores = np.zeros(n_points)
+
+    for i in range(n_points):
+        # Get neighbors (excluding self)
+        neighbor_distances = distances[i, 1:]
+
+        # Convert distances to similarities (for cosine: similarity = 1 - distance)
+        if metric == "cosine":
+            similarities = 1.0 - neighbor_distances
+        else:
+            # For euclidean, use inverse distance (with smoothing)
+            similarities = 1.0 / (1.0 + neighbor_distances)
+
+        # Density = average similarity to neighbors
+        # Higher similarity means the term is in a dense region (more generic)
+        density_scores[i] = similarities.mean()
+
+    density_scores = np.log(density_scores)
+    # Normalize density scores to [0, 1] range
+    if density_scores.max() > density_scores.min():
+        density_scores_norm = (density_scores - density_scores.min()) / (
+            density_scores.max() - density_scores.min()
+        )
+    else:
+        density_scores_norm = np.ones_like(density_scores)
+
+    # Compute label simplicity scores
+    if word_frequencies is None:
+        word_frequencies = {}
+
+    simplicity_scores = np.zeros(n_points)
+    for i, label in enumerate(labels):
+        simplicity_metrics = _measure_label_simplicity(
+            str(label), word_frequencies=word_frequencies
+        )
+        simplicity_scores[i] = simplicity_metrics["simplicity_score"]
+
+    simplicity_scores = np.log(simplicity_scores)
+
+    # Normalize simplicity scores to [0, 1] range
+    if simplicity_scores.max() > simplicity_scores.min():
+        simplicity_scores_norm = (simplicity_scores - simplicity_scores.min()) / (
+            simplicity_scores.max() - simplicity_scores.min()
+        )
+    else:
+        simplicity_scores_norm = np.ones_like(simplicity_scores)
+
+    # Combine density and simplicity scores
+    # Shorter, simpler terms should be preferred even if density is similar
+    generality_scores = (
+        density_weight * density_scores_norm
+        + (1 - density_weight) * simplicity_scores_norm
+    )
+
+    return generality_scores
diff --git a/pelinker/text/mentions.py b/pelinker/text/mentions.py
new file mode 100644
index 0000000..895c34b
--- /dev/null
+++ b/pelinker/text/mentions.py
@@ -0,0 +1,154 @@
+"""KB mention detection without an encoder: one definition of "a mention" for every caller.
+
+Stage (A) of the fit labels mentions to train on; the gold sampler counts mentions to
+stratify abstracts. Both must agree on what a mention is, or the sample is stratified on
+something the model is never trained to find. This module is that shared definition:
+
+- **verb-predicate labels** are matched from the dependency parse, one label per verb,
+  chosen by voice (:class:`pelinker.text.predicates.PredicateMatcher`);
+- **other labels** ("part of", "has part") are matched on lemma windows;
+- **one label per mention site** (:func:`keep_one_per_site`): a span nested in a longer
+  match is dropped, and on an identical span a parse-based label beats a lexical one.
+
+No embeddings are computed here; :mod:`pelinker.text.embed` adds them for the fit.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+from collections.abc import Callable, Iterable
+from typing import TypeVar
+
+from pelinker.core.onto import SimplifiedToken
+from pelinker.text.predicates import PredicateMatcher, PredicateSpec, predicate_spec
+from pelinker.text.tokenize import text_to_tokens
+
+LEXICAL_RULE = "lexical"
+# Bump when detection rules change: caches of detected mentions (the gold sampler's pool)
+# key on it, so a stale cache is recomputed rather than silently reused.
+DETECTOR_VERSION = "2"
+MAX_LEXICAL_WINDOW = 4
+
+T = TypeVar("T")
+
+
+@dataclasses.dataclass(frozen=True)
+class Mention:
+    """A labelled KB mention; ``a``/``b`` are character offsets into the parsed text."""
+
+    a: int
+    b: int
+    label: str
+    direction: str | None
+    rule: str
+
+    @property
+    def is_verbal(self) -> bool:
+        return self.rule != LEXICAL_RULE
+
+
+def split_predicate_labels(
+    labels: Iterable[str],
+    nlp,
+    symmetric_labels: frozenset[str] = frozenset(),
+    *,
+    tokenize: Callable[..., list[SimplifiedToken]] = text_to_tokens,
+) -> tuple[list[PredicateSpec], list[str]]:
+    """Verb-predicate labels (matched from the parse) and the rest (matched lexically)."""
+    specs: list[PredicateSpec] = []
+    lexical: list[str] = []
+    for label in labels:
+        spec = predicate_spec(
+            label, nlp, symmetric=label in symmetric_labels, tokenize=tokenize
+        )
+        if spec is None:
+            lexical.append(label)
+        else:
+            specs.append(spec)
+    return specs, lexical
+
+
+def keep_one_per_site(
+    items: list[T],
+    *,
+    span: Callable[[T], tuple[int, int]],
+    preference: Callable[[T], tuple],
+) -> list[T]:
+    """Keep one item per mention site.
+
+    An item whose span lies strictly inside another item's span is dropped — the longer,
+    more specific label wins ("regulates transport of" over "regulates"). On an identical
+    span the smallest ``preference`` wins, so the result does not depend on input order.
+    """
+    if len(items) < 2:
+        return items
+    by_span: dict[tuple[int, int], T] = {}
+    for item in items:
+        key = span(item)
+        if key not in by_span or preference(item) < preference(by_span[key]):
+            by_span[key] = item
+    spans = list(by_span)
+    kept = [
+        item
+        for key, item in by_span.items()
+        if not any(o != key and o[0] <= key[0] and key[1] <= o[1] for o in spans)
+    ]
+    kept.sort(key=span)
+    return kept
+
+
+class MentionDetector:
+    """KB mentions in parsed text: parse-based for verb labels, lexical for the rest."""
+
+    def __init__(
+        self,
+        labels: Iterable[str],
+        nlp,
+        symmetric_labels: frozenset[str] = frozenset(),
+    ) -> None:
+        specs, lexical = split_predicate_labels(labels, nlp, symmetric_labels)
+        self.matcher = PredicateMatcher(specs)
+        self.lexical_index: dict[tuple[str, ...], str] = {}
+        for label in lexical:
+            lemmas = tuple(t.lemma.lower() for t in text_to_tokens(nlp, label))
+            if 0 < len(lemmas) <= MAX_LEXICAL_WINDOW:
+                self.lexical_index.setdefault(lemmas, label)
+        self._widths = sorted({len(k) for k in self.lexical_index})
+
+    @property
+    def verb_labels(self) -> frozenset[str]:
+        return self.matcher.labels
+
+    def detect(self, tokens: list[SimplifiedToken]) -> list[Mention]:
+        """Mentions in one parsed text, one per site, sorted by position."""
+        found: list[Mention] = []
+        by_i = {t.i: t for t in tokens if t.i is not None}
+        for m in self.matcher.match(tokens):
+            found.append(
+                Mention(
+                    a=by_i[m.start].ix,
+                    b=by_i[m.head].ix_end,
+                    label=m.label,
+                    direction=m.direction,
+                    rule=m.rule,
+                )
+            )
+        lemmas = [t.lemma.lower() for t in tokens]
+        for width in self._widths:
+            for k in range(len(tokens) - width + 1):
+                label = self.lexical_index.get(tuple(lemmas[k : k + width]))
+                if label is not None:
+                    found.append(
+                        Mention(
+                            a=tokens[k].ix,
+                            b=tokens[k + width - 1].ix_end,
+                            label=label,
+                            direction=None,
+                            rule=LEXICAL_RULE,
+                        )
+                    )
+        return keep_one_per_site(
+            found,
+            span=lambda m: (m.a, m.b),
+            preference=lambda m: (0 if m.is_verbal else 1, m.label),
+        )
diff --git a/pelinker/text/models.py b/pelinker/text/models.py
new file mode 100644
index 0000000..f4774be
--- /dev/null
+++ b/pelinker/text/models.py
@@ -0,0 +1,90 @@
+from transformers import AutoModel, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+
+
+"""Encoder/tokenizer loading and hidden-state layer specifications."""
+
+
+def load_models(model_type, sentence=False):
+    if model_type == "scibert":
+        spec = "allenai/scibert_scivocab_cased"
+    elif model_type == "biobert":
+        spec = "dmis-lab/biobert-base-cased-v1.2"
+    elif model_type == "pubmedbert":
+        spec = "neuml/pubmedbert-base-embeddings"
+    elif model_type == "biobert-stsb":
+        spec = "pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb"
+    elif model_type == "bert":
+        spec = "google-bert/bert-base-uncased"
+    elif model_type == "bluebert":
+        spec = "bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12"
+    else:
+        raise ValueError(f"{model_type} unsupported")
+    if sentence:
+        tokenizer, model = None, SentenceTransformer(spec)
+    else:
+        tokenizer, model = (
+            AutoTokenizer.from_pretrained(spec),
+            AutoModel.from_pretrained(spec),
+        )
+    return tokenizer, model
+
+
+def normalize_layers_spec(
+    layers_spec: str | list[int],
+    *,
+    n_hidden_states: int | None = None,
+) -> list[int]:
+    """Parse and validate indices for the stacked ``hidden_states`` tensor.
+
+    String form follows the same convention as historical :func:`str2layers`: each
+    digit is a distinct layer counted from the end, e.g. ``\"1\"`` → ``[-1]``,
+    ``\"12\"`` → ``[-2, -1]``. Commas in the string are ignored.
+
+    Args:
+        layers_spec: Digit-only string or list of **negative** indices (HF convention).
+        n_hidden_states: If set (length of first dim of stacked hidden states), indices
+            must satisfy ``layer >= -n_hidden_states``.
+
+    Returns:
+        Sorted unique negative layer indices.
+
+    Raises:
+        ValueError: Empty spec, positive indices, ``\"sent\"``, or out-of-range indices.
+    """
+    if isinstance(layers_spec, str):
+        spec = layers_spec.strip()
+        if not spec:
+            raise ValueError("layers_spec string is empty")
+        if spec == "sent":
+            raise ValueError(
+                "layers_spec 'sent' is not valid for transformer hidden-state pooling"
+            )
+        if "," in spec:
+            spec = "".join(spec.split(","))
+        if not spec.isdigit():
+            raise ValueError(
+                f"layers_spec string must be digits only (e.g. '1' or '12'), got {layers_spec!r}"
+            )
+        layers = sorted({-abs(int(ch)) for ch in spec})
+    else:
+        if not layers_spec:
+            raise ValueError("layers_spec list is empty")
+        for i, layer in enumerate(layers_spec):
+            if layer >= 0:
+                raise ValueError(
+                    f"layer index must be negative (HF hidden_states convention), got {layer} at position {i}"
+                )
+        layers = sorted(set(layers_spec))
+    if n_hidden_states is not None:
+        for layer in layers:
+            if layer < -n_hidden_states:
+                raise ValueError(
+                    f"layer {layer} is out of range for {n_hidden_states} stacked hidden states"
+                )
+    return layers
+
+
+def str2layers(layers_spec: str | list[int]) -> list[int]:
+    """Parse layer specification; same rules as :func:`normalize_layers_spec`."""
+    return normalize_layers_spec(layers_spec, n_hidden_states=None)
diff --git a/pelinker/text/predicates.py b/pelinker/text/predicates.py
new file mode 100644
index 0000000..6b20e81
--- /dev/null
+++ b/pelinker/text/predicates.py
@@ -0,0 +1,323 @@
+"""Voice-aware detection of KB predicate mentions, for weak supervision.
+
+Matching a KB label against every contiguous lemma window of the text cannot tell voice:
+"X is activated by Y" matches both ``activates`` (the 1-gram ``activated``) and
+``activated by`` (the 2-gram), so one mention carries both a relation and its converse.
+A window cannot see an agent separated from its verb ("activated in vivo by") or a
+passive with no agent at all ("NF-kB was activated"), and it cannot tell the verb
+"controls" from the noun "controls".
+
+This module reads the dependency parse instead. A KB label that is a verb predicate is
+reduced to a :class:`PredicateSpec` — head verb lemma, ``-ly`` modifiers, trailing
+preposition, and whether the label is itself a passive form ("regulated by"). A verb token
+in the text gets **at most one** label:
+
+- its voice is decided from the parse (passive auxiliary, passive subject, ``by`` agent,
+  reduced relative; a prenominal participle — "activated T cells" — is dropped);
+- the label whose own reading has the same voice is preferred ("activated by" for a
+  passive mention); failing that, the opposite-voice label is used with
+  ``direction="inverse"``;
+- a label's preposition is part of its meaning, so it must attach to the verb in the
+  text ("developed complications" is not ``develops from``; "occur during" is not
+  ``occurs across``). The one exception is the agent of a passive ``by`` label, which
+  may be absent ("NF-kB was activated");
+- among candidates, the most specific wins: every modifier of the label must modify the
+  verb in the text;
+- a symmetric relation is never flipped: its direction is ``symmetric``.
+
+The embedded span is the verb token, extended to the label's modifier when that modifier
+immediately precedes it ("negatively regulated"). This matches what prediction proposes —
+contiguous windows embedded by the encoder — so the parse is used to *label* training
+mentions and never as a feature.
+
+Labels that are not verb predicates ("part of", "has part", "capable of") are left to the
+caller's lexical matching.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+from collections import defaultdict
+from collections.abc import Callable
+
+from pelinker.core.onto import SimplifiedToken
+from pelinker.text.tokenize import text_to_tokens
+
+PREPOSITIONS = ("by", "in", "of", "from", "to", "for", "with", "at", "on")
+# A verb label may end in any of these; only ``PREPOSITIONS`` mark a passive form.
+_LABEL_PREPOSITIONS = PREPOSITIONS + (
+    "into",
+    "across",
+    "after",
+    "before",
+    "during",
+    "through",
+    "upon",
+    "onto",
+    "within",
+    "against",
+    "via",
+)
+
+# Past participles that end in neither "-ed" nor "-en" ("bound by" ↔ "binds to").
+IRREGULAR_PARTICIPLES = frozenset(
+    {
+        "bound",
+        "bred",
+        "brought",
+        "built",
+        "caught",
+        "cut",
+        "fed",
+        "found",
+        "held",
+        "kept",
+        "led",
+        "left",
+        "lost",
+        "made",
+        "met",
+        "put",
+        "sent",
+        "set",
+        "shut",
+        "sought",
+        "split",
+        "spread",
+        "struck",
+        "stuck",
+        "taught",
+        "told",
+        "wound",
+    }
+)
+
+_COPULAS = frozenset({"is", "are", "be", "was", "were"})
+
+# Dependency labels that mark a passive clause on the verb's children.
+_PASSIVE_CHILD_DEPS = frozenset({"auxpass", "nsubjpass", "agent"})
+_PREP_CHILD_DEPS = frozenset({"prep", "agent", "dative", "prt"})
+
+DIRECTION_FORWARD = "forward"
+DIRECTION_INVERSE = "inverse"
+DIRECTION_SYMMETRIC = "symmetric"
+
+
+def is_passive_form(label: str) -> bool:
+    """Whether a label reads as a participle plus preposition ("regulated by")."""
+    parts = label.split()
+    if len(parts) < 2 or parts[-1].lower() not in PREPOSITIONS:
+        return False
+    head = parts[-2].lower()
+    return head.endswith("ed") or head.endswith("en") or head in IRREGULAR_PARTICIPLES
+
+
+@dataclasses.dataclass(frozen=True)
+class PredicateSpec:
+    """A KB label reduced to what a verb mention has to agree with."""
+
+    label: str
+    head_lemma: str
+    modifiers: tuple[str, ...]
+    prep: str | None
+    passive: bool
+    symmetric: bool = False
+
+
+@dataclasses.dataclass(frozen=True)
+class PredicateMatch:
+    """One labelled verb mention; token indices are positions in the parsed chunk."""
+
+    label: str
+    head: int
+    start: int
+    direction: str
+    rule: str
+
+
+def predicate_spec(
+    label: str,
+    nlp,
+    *,
+    symmetric: bool = False,
+    tokenize: Callable[..., list[SimplifiedToken]] = text_to_tokens,
+) -> PredicateSpec | None:
+    """Reduce ``label`` to a verb predicate, or ``None`` when it is not one.
+
+    The head is lemmatized inside a template sentence rather than alone: a bare label
+    ("regulates", "controls") is as likely to be tagged a noun as a verb, and its lemma
+    then depends on that guess. ``tokenize`` is the ``(nlp, text)`` tokenizer to use.
+    """
+    words = label.split()
+    while len(words) > 1 and words[0].lower() in _COPULAS:
+        words = words[1:]
+    prep = (
+        words[-1].lower()
+        if len(words) > 1 and words[-1].lower() in _LABEL_PREPOSITIONS
+        else None
+    )
+    core = words[:-1] if prep else words
+    if not core:
+        return None
+    *mods, head = core
+    if any(not m.lower().endswith("ly") for m in mods):
+        return None
+    passive = prep is not None and is_passive_form(f"{head} {prep}")
+    # KB labels write active verbs in the third person ("regulates", "binds to"). The
+    # template below forces a verb reading on whatever fills the slot, so the surface
+    # form is what keeps "is tautomer of" or "coincident with" out.
+    if not passive and not head.lower().endswith("s"):
+        return None
+    if passive:
+        template = f"They were {head} {prep} it ."
+    elif prep:
+        template = f"It {head} {prep} them ."
+    else:
+        template = f"It {head} them ."
+    token = next((t for t in tokenize(nlp, template) if t.text == head), None)
+    if token is None or token.pos != "VERB":
+        return None
+    return PredicateSpec(
+        label=label,
+        head_lemma=token.lemma.lower(),
+        modifiers=tuple(m.lower() for m in mods),
+        prep=prep,
+        passive=passive,
+        symmetric=symmetric,
+    )
+
+
+def verb_voice(
+    token: SimplifiedToken, children: list[SimplifiedToken]
+) -> tuple[str, str] | None:
+    """``(voice, rule)`` for a verb token, or ``None`` when voice cannot be told.
+
+    A prenominal modifier ("activated T cells", "TNF-induced apoptosis", "binding sites")
+    is dropped: it names a state or a kind, not a relation between two arguments, and its
+    orientation is not recoverable from the parse. A present participle that takes a
+    direct object is kept, as an active mention — spaCy sometimes attaches a verb phrase
+    ("the kinase phosphorylating Akt") as a modifier. A bare ``VBN`` with neither passive
+    marking nor a perfect ``have`` is dropped for the same reason.
+    """
+    deps = {c.dep for c in children}
+    if token.dep == "amod" and (token.tag == "VBN" or "dobj" not in deps):
+        return None
+    if "agent" in deps:
+        return "passive", "passive_agent"
+    if deps & _PASSIVE_CHILD_DEPS:
+        return "passive", "passive_agentless"
+    if token.tag == "VBN":
+        if token.dep == "acl":
+            return "passive", "reduced_relative"
+        if any(c.dep == "aux" and c.lemma.lower() == "have" for c in children):
+            return "active", "active"
+        return None
+    return "active", "active"
+
+
+class PredicateMatcher:
+    """One label per verb mention, from the parse; see the module docstring."""
+
+    def __init__(self, specs: list[PredicateSpec]) -> None:
+        self._by_lemma: dict[str, list[PredicateSpec]] = defaultdict(list)
+        for spec in specs:
+            self._by_lemma[spec.head_lemma].append(spec)
+
+    @property
+    def labels(self) -> frozenset[str]:
+        return frozenset(s.label for specs in self._by_lemma.values() for s in specs)
+
+    def match(self, tokens: list[SimplifiedToken]) -> list[PredicateMatch]:
+        """Labelled verb mentions in one parsed chunk; ``[]`` if the parse is missing."""
+        parsed = [
+            (t.i, t.head_i, t)
+            for t in tokens
+            if t.i is not None and t.head_i is not None
+        ]
+        if not parsed or len(parsed) != len(tokens):
+            return []
+        by_i = {i: t for i, _, t in parsed}
+        children: dict[int, list[SimplifiedToken]] = defaultdict(list)
+        for i, head_i, t in parsed:
+            if head_i != i:
+                children[head_i].append(t)
+
+        matches: list[PredicateMatch] = []
+        for i, _, token in parsed:
+            if token.pos != "VERB":
+                continue
+            specs = self._by_lemma.get(token.lemma.lower())
+            if not specs:
+                continue
+            kids = children.get(i, [])
+            voiced = verb_voice(token, kids)
+            if voiced is None:
+                continue
+            voice, rule = voiced
+            chosen = _choose(specs, kids, passive=voice == "passive")
+            if chosen is None:
+                continue
+            spec, same_voice = chosen
+            if spec.symmetric:
+                direction = DIRECTION_SYMMETRIC
+            elif same_voice:
+                direction = DIRECTION_FORWARD
+            else:
+                direction = DIRECTION_INVERSE
+            matches.append(
+                PredicateMatch(
+                    label=spec.label,
+                    head=i,
+                    start=_span_start(i, spec, by_i),
+                    direction=direction,
+                    rule=rule,
+                )
+            )
+        return matches
+
+
+def _choose(
+    specs: list[PredicateSpec], children: list[SimplifiedToken], *, passive: bool
+) -> tuple[PredicateSpec, bool] | None:
+    """Best label for one verb: same voice first, then the converse reading."""
+    adverbs = {c.lemma.lower() for c in children if c.dep == "advmod"}
+    preps = {c.lemma.lower() for c in children if c.dep in _PREP_CHILD_DEPS}
+
+    def prep_satisfied(s: PredicateSpec) -> bool:
+        return (
+            s.prep is None
+            or s.prep in preps
+            or (s.passive and s.prep == "by")  # agentless passive
+        )
+
+    def best(pool: list[PredicateSpec]) -> PredicateSpec | None:
+        fitting = [s for s in pool if set(s.modifiers) <= adverbs and prep_satisfied(s)]
+        if not fitting:
+            return None
+
+        def rank(s: PredicateSpec) -> tuple[int, str]:
+            score = 2 * len(s.modifiers) + (1 if s.prep and s.prep in preps else 0)
+            return (-score, s.label)
+
+        return min(fitting, key=rank)
+
+    same = best([s for s in specs if s.passive == passive])
+    if same is not None:
+        return same, True
+    other = best([s for s in specs if s.passive != passive])
+    if other is not None:
+        return other, False
+    return None
+
+
+def _span_start(i: int, spec: PredicateSpec, by_i: dict[int, SimplifiedToken]) -> int:
+    """Extend the span to the label's modifier when it immediately precedes the verb."""
+    prev = by_i.get(i - 1)
+    if (
+        prev is not None
+        and spec.modifiers
+        and prev.head_i == i
+        and prev.lemma.lower() in spec.modifiers
+    ):
+        return i - 1
+    return i
diff --git a/pelinker/text/tokenize.py b/pelinker/text/tokenize.py
new file mode 100644
index 0000000..a16a68b
--- /dev/null
+++ b/pelinker/text/tokenize.py
@@ -0,0 +1,211 @@
+import bisect
+import re
+
+from string import punctuation, whitespace
+
+from pelinker.core.onto import (
+    Expression,
+    SimplifiedToken,
+    WordGrouping,
+)
+
+"""spaCy tokenization, sentence splitting, and word/subword span mapping."""
+
+
+def map_spans_to_spans_basic(
+    words_boundaries, token_boundaries
+) -> dict[tuple[int, int], list[int]]:
+    """Map each word character span to tokenizer subword indices.
+
+    Both spans use half-open intervals ``[start, end)`` in character offsets. A
+    subword token belongs to a word span iff the two intervals have positive overlap.
+
+    Word spans may overlap (sliding W2/W3 windows). A single forward pointer over
+    tokens is incorrect in that case; each word span is matched independently.
+
+    Args:
+        words_boundaries: Sequence of ``(wa, wb)`` word/window spans.
+        token_boundaries: Sequence of ``(ta, tb)`` subword spans covering the chunk.
+
+    Returns:
+        Dict ``(wa, wb) -> [token_index, ...]`` in ascending token order.
+    """
+
+    spans = [(int(ta), int(tb)) for ta, tb in token_boundaries]
+    nonempty = [(j, ta, tb) for j, (ta, tb) in enumerate(spans) if tb > ta]
+    idx = [j for j, _, _ in nonempty]
+    starts = [ta for _, ta, _ in nonempty]
+    ends = [tb for _, _, tb in nonempty]
+    ordered = all(a <= b for a, b in zip(starts, starts[1:])) and all(
+        a <= b for a, b in zip(ends, ends[1:])
+    )
+
+    map_ix_jx: dict[tuple[int, int], list[int]] = {}
+    for ix_word in words_boundaries:
+        wa, wb = ix_word
+        if ordered:
+            # tokens overlapping [wa, wb) are the contiguous run with end > wa, start < wb
+            lo = bisect.bisect_right(ends, wa)
+            hi = bisect.bisect_left(starts, wb, lo=lo)
+            map_ix_jx[ix_word] = idx[lo:hi]
+        else:
+            map_ix_jx[ix_word] = [j for j, ta, tb in nonempty if ta < wb and tb > wa]
+
+    return map_ix_jx
+
+
+def tokens_from_doc(doc) -> list[SimplifiedToken]:
+    """spaCy ``Doc`` → :class:`SimplifiedToken` list (use with ``nlp.pipe``)."""
+    return [
+        SimplifiedToken(
+            **{
+                "lemma": token.lemma_,
+                "text": token.text,
+                "tag": token.tag_,
+                "pos": token.pos_,
+                "is_stop": bool(token.is_stop),
+                "i": token.i,
+                "head_i": token.head.i,
+                "dep": token.dep_,
+                "ix": token.idx,
+                "ix_end": token.idx + len(token),
+            }
+        )
+        for token in doc
+    ]
+
+
+def text_to_tokens(nlp, text) -> list[SimplifiedToken]:
+    return tokens_from_doc(nlp(text))
+
+
+def keep_expression_for_prediction(expr: Expression) -> bool:
+    """Whether to keep a sliding-window mention for :meth:`~pelinker.model.Linker.predict`.
+
+    Drops any window that contains punctuation (spaCy ``pos_ == "PUNCT"``). Drops
+    windows whose tokens are **all** stop words; keeps windows that mix content and
+    function words (e.g. ``type of``).
+    """
+    toks = expr.tokens
+    if not toks:
+        return False
+    if any(t.pos == "PUNCT" for t in toks if t.pos is not None):
+        return False
+    if all(t.is_stop is True for t in toks):
+        return False
+    return True
+
+
+def split_into_sentences(text):
+    text = re.sub(r"\s+", " ", text)
+
+    # split on .!? if followed by a capital and not preceded by a capital
+    pat = r"(?<=[^A-Z][.!?])\s*(?=[A-Z])"
+    phrases_ = re.split(pat, text)
+    # trim initial/terminal whitespaces
+    trim_whitespace = re.compile(r"^[\s+]+|[\s+]+$")
+    phrases_ = [trim_whitespace.sub("", p) for p in phrases_]
+    return phrases_
+
+
+def map_words_to_tokens(
+    text_token_spans: list[tuple[int, int]], text_word_spans: list[tuple[int, int]]
+) -> tuple[list[tuple[int, int]], list[tuple[int, int]]]:
+    """
+    given text token and word spans,
+
+        words : [...(start_pos_i, end_pos_i)...]
+        tokens : [...(start_pos_i, end_pos_i)...]
+
+    we define work -> token spans, i.e. a mapping of which tokens belong to words groups
+
+    if there is a positive overlap between word i and token j spans,
+        we consider that token j belongs to work i group
+
+    return a list of work -> token bounds : [...(start_token_i, end_token_i)...]
+    and a refreshed
+
+
+    """
+
+    map_ix_jx = map_spans_to_spans_basic(text_word_spans, text_token_spans)
+
+    # sanitize token offsets
+    map_ix_jx = {k: v for k, v in map_ix_jx.items() if v}
+    text_word_spans = [x for x in map_ix_jx.keys()]
+    token_word_spans = [(y[0], y[-1] + 1) for y in map_ix_jx.values()]
+    return text_word_spans, token_word_spans
+
+
+def get_word_boundaries(text) -> list[tuple[int, int]]:
+    """
+        render word boundaries in text
+
+    :param text:
+    :return:
+    """
+
+    ix_whitespaces = [0] + [
+        i + 1
+        for i, char in enumerate(text)
+        if char in whitespace or char in punctuation
+    ]
+    if text[-1] not in whitespace and text[-1] not in punctuation:
+        ix_whitespaces += [len(text) + 1]
+
+    ix_words = [(i, j - 1) for i, j in zip(ix_whitespaces, ix_whitespaces[1:])]
+    ix_words = [(i, j) for i, j in ix_words if i != j]
+    return ix_words
+
+
+def token_list_with_window(
+    tokens: list[SimplifiedToken], window: WordGrouping, itext=None, ichunk=None
+) -> list[Expression]:
+    """Build every contiguous ``window``-token slice as an :class:`~pelinker.core.onto.Expression`.
+
+    Each expression stores the participating :class:`~pelinker.core.onto.SimplifiedToken`
+    objects and, after ``__post_init__``, character bounds ``a``/``b`` for the span.
+
+    Args:
+        tokens: spaCy-derived tokens for one chunk.
+        window: Window size via :class:`~pelinker.core.onto.WordGrouping` (``W1`` → 1 token, etc.).
+        itext: Document index in the outer batch (optional metadata on expressions).
+        ichunk: Chunk index within the document (optional metadata).
+
+    Returns:
+        Length ``len(tokens) - w + 1`` list of expressions (empty if ``len(tokens) < w``).
+    """
+    agg = []
+    w = int(window.value)
+    for k in range(len(tokens) - w + 1):
+        agg.append(Expression(tokens=tokens[k : k + w], itext=itext, ichunk=ichunk))
+    return agg
+
+
+def map_words_to_tokens_list(
+    text_token_spans_list: list[list[tuple[int, int]]],
+    text_word_spans_list: list[list[tuple[int, int]]],
+):
+    """
+    take a batch of token spans and a batch of word spans
+
+    return a batch of work to token maps
+        and also an updated batch of word spans
+            (some words can not be mapped to tokens, so they are excluded)
+
+
+    """
+
+    text_word_spans_list_: list[list[tuple[int, int]]] = []
+    token_work_spans_list: list[list[tuple[int, int]]] = []
+
+    for text_token_spans, text_word_spans in zip(
+        text_token_spans_list, text_word_spans_list
+    ):
+        text_word_spans, token_word_spans = map_words_to_tokens(
+            text_token_spans, text_word_spans
+        )
+        text_word_spans_list_ += [text_word_spans]
+        token_work_spans_list += [token_word_spans]
+
+    return token_work_spans_list, text_word_spans_list_
diff --git a/pyproject.toml b/pyproject.toml
index f8c82b4..f4bfabd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,23 +6,25 @@ requires = ["hatchling"]
 authors = [{email = "abelikov@gmail.com", name = "Alexander Belikov"}]
 dependencies = [
   "click>=8.1,<9",
-  "cupy-cuda12x>=13.6.0",
   "dataclass-wizard>=0.35.1",
   "fastapi>=0.115.0,<1",
   "hdbscan>=0.8.41",
   "hydra-core>=1.3.2",
   "joblib>=1.4.2,<2",
+  "matplotlib>=3.9",
   "pandas>=2.3.3",
-  "pip>=26.0.1",
+  "plotly>=6.2.0,<7",
   "pyarrow>=23.0.1",
+  "pysankey>=0.0.1",
   "rich>=14.1.0",
   "scikit-learn>=1.5.0,<2",
   "scikit-optimize>=0.10.2",
-  "sentence-transformers>=3.0.1,<4",
+  "seaborn>=0.13.2,<0.14",
+  "sentence-transformers>=5.6,<6",
   "spacy>=3.7.5,<4",
   "tf-keras>=2.16.0",
-  "torch>=2.9.1",
-  "transformers>=4.41.2,<5",
+  "torch>=2.13.0",
+  "transformers>=5.5.0,<6",
   "umap-learn[parametric-umap]>=0.5.11",
   "uvicorn>=0.42.0",
   "wordfreq>=3.1.1"
@@ -35,13 +37,12 @@ version = "0.2.0"
 
 [project.optional-dependencies]
 dev = [
-  "bumplot>=0.2.1",
-  "mypy>=1.11",
-  "plotly>=6.2.0,<7",
+  # Pinned as a real dependency rather than left to `spacy download`: any `uv sync`
+  # prunes a downloaded model, which silently re-skips every test that needs it.
+  "en-core-web-lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.8.0/en_core_web_lg-3.8.0-py3-none-any.whl",
   "pre-commit>=4.0.1,<5",
-  "pysankey>=0.0.1",
   "pytest>=9.0.2",
-  "seaborn>=0.13.2,<0.14"
+  "ty>=0.0.1a10"
 ]
 docs = [
   "griffe<2.0.0",
@@ -54,8 +55,29 @@ docs = [
   "mkdocstrings-python>=2.0.3",
   "mkdocstrings[python]>=0.29.1"
 ]
-gpu = [
-  "cupy-cuda12x>=13.6.0"
+eval = [
+  # LLM-assisted gold pre-annotation and the LLM linking baseline; the linker itself
+  # never calls an LLM. Every provider seam in pelinker/eval/llm.py ships here: the
+  # agreement slice needs a second model *family*, and the linking baseline a third
+  # model that did not annotate the gold, so one SDK is never enough in practice.
+  "anthropic>=0.40",
+  "google-genai>=1.0",
+  "openai>=1.60"
+]
+# GPU extras are mutually exclusive; pick the one matching the "CUDA Version" that
+# `nvidia-smi` reports (the driver's ceiling — an installed toolkit is not used).
+gpu-cu12 = [
+  "cupy-cuda12x[ctk]>=14.2.0",
+  "torch>=2.13.0"
+]
+gpu-cu13 = [
+  "cupy-cuda13x[ctk]>=14.2.0",
+  "torch>=2.13.0"
+]
+preprocess = [
+  # KB regeneration only (run/preprocessing/extract_properties_*.py); the linker never
+  # parses ontologies at fit or serve time.
+  "rdflib>=7.0"
 ]
 
 [project.scripts]
@@ -73,11 +95,31 @@ include = ["pelinker"]
 [tool.hatch.build.targets.wheel]
 include = ["pelinker"]
 
+[tool.hatch.metadata]
+# the pinned en_core_web_lg wheel in the `dev` extra is a direct URL reference
+allow-direct-references = true
+
 [tool.pytest.ini_options]
 addopts = "-q"
 markers = [
-  "heavy: needs large out-of-band assets (spaCy en_core_web_trf, HF weights); skipped automatically when absent"
+  "heavy: needs large out-of-band assets (spaCy en_core_web_lg, HF weights); skipped automatically when absent"
 ]
 testpaths = ["test"]
 
+[tool.ruff.lint]
+# The pre-commit defaults were not reporting unused imports, which is how ~100 of them
+# accumulated. F401 is the point of this block; the rest is ruff's default set.
+ignore = ["E722"]
+select = ["E4", "E7", "E9", "F"]
+
 [tool.uv]
+conflicts = [[{extra = "gpu-cu12"}, {extra = "gpu-cu13"}]]
+
+[[tool.uv.index]]
+explicit = true
+name = "pytorch-cu126"
+url = "https://download.pytorch.org/whl/cu126"
+
+[tool.uv.sources]
+# PyPI's Linux torch is built against CUDA 13; CUDA 12 drivers need the cu126 build.
+torch = [{extra = "gpu-cu12", index = "pytorch-cu126"}]
diff --git a/run/README.md b/run/README.md
index 98e11ea..267a677 100644
--- a/run/README.md
+++ b/run/README.md
@@ -1,6 +1,6 @@
 # Run Scripts Documentation
 
-This directory contains scripts for preprocessing knowledge bases, embedding corpora, analyzing embedding quality, and producing **OOV / manifold anomaly** figures from fit reports plus batch-linked mention dumps.
+This directory contains scripts for preprocessing knowledge bases, embedding corpora, analyzing embedding quality, producing **OOV / manifold anomaly** figures from fit reports plus batch-linked mention dumps, and building and scoring the **human-verified gold set** (`eval/`).
 
 For a **single end-to-end train** (corpus embedding → KB filtering/aggregation → PCA/UMAP → optimized HDBSCAN clustering → serialized linker artifact), use the packaged CLI `pelinker.cli.fit` documented in [Fitting the linker model](#fitting-the-linker-model) below.
 
@@ -11,28 +11,36 @@ The published **MkDocs** site mirrors this layout at a high level under [Run scr
 ```
 run/
 ├── README.md                    # This file
-├── embed_kb_corpus.py          # Embed knowledge base corpus
-├── test_server.py              # Smoke-test pelinker.cli.server HTTP routes
-├── loop.embed.kb.corpus.sh     # Batch embedding (grid over model × layer)
-├── loop.fit.sh                 # Batch full fit: same grid, runs pelinker-fit (A+B)
+├── embed_kb_corpus.py           # Embed a corpus against the KB (fit stage A, standalone)
+├── smoke_server.py              # Smoke-test pelinker.cli.server HTTP routes
+├── loop.embed.kb.corpus.sh      # Batch embedding (grid over model × layer)
+├── loop.fit.sh                  # Batch full fit: same grid, runs pelinker-fit (A+B)
 ├── preprocessing/               # Property knowledge base generation
-│   ├── extract_properties_go.py    # Extract from GO-CAMs ontology
-│   ├── extract_properties_ro.py    # Extract from Relations Ontology
-│   └── merge_properties.py         # Merge properties from all sources
-├── analysis/                    # Embedding quality & OOV diagnostics
-│   ├── model_selection.py          # Model selection over embedding combinations
-│   ├── dim_selection.py            # PCA/UMAP dim search for one embedding combo
+│   ├── extract_properties_go.py    # Extract from the GO-CAMs ontology
+│   ├── extract_properties_ro.py    # Extract from the Relations Ontology
+│   ├── merge_properties.py         # Merge properties from all sources
+│   └── derive_inverse_pairs.py     # Link converse pairs, elect a canonical member
+├── analysis/                    # Embedding quality, stability & OOV diagnostics
+│   ├── cluster_stability.py        # Cluster assignment stability across draws
 │   ├── compact_predict_study.py    # Legacy vs ParametricUMAP+MLP quality/size gates
+│   ├── direction_diagnostic.py     # Where voice/direction is lost (encoder vs supervision)
+│   ├── weak_label_check.py         # Acceptance check for stage-(A) weak labels
 │   ├── oov_analysis.py             # Fit report + OOV mention dump → PDF figures
-│   ├── replot_dbcv_ari_scatter.py  # DBCV vs ARI scatter from existing grid CSV
+│   ├── replot_fit.py               # Re-render figures from an existing fit report
 │   └── select_diverse_entities.py  # Select diverse entity subsets
-└── obsolete/                    # Deprecated scripts (not actively maintained)
-    ├── analysis/
-    ├── experiments/
-    ├── preprocessing/
-    └── testing/
+└── eval/                        # Human-verified gold set & reference baselines
+    ├── sample_gold.py              # Eligibility gate + stratified sample, grown in batches
+    ├── audit_sample.py             # Sample soundness + KB coverage, after every batch
+    ├── annotate_llm.py             # LLM pre-annotation of predicate mentions
+    ├── gold_review_sheet.py        # Review TSV round trip + Cohen's κ
+    ├── run_baselines.py            # Lexical / encoder / LLM / linker, one harness
+    ├── dataset_stats.py            # Corpus, KB, fit-report and gold statistics
+    └── prompts/                    # Annotation prompt templates
 ```
 
+Model selection, dimension search, the scale curve and grid replotting are **console
+scripts**, not files under `run/` — see [Hyperparameter search](#hyperparameter-search).
+
 ## Preprocessing Scripts
 
 Scripts in the `preprocessing/` directory generate property knowledge base files from various ontology sources.
@@ -52,8 +60,8 @@ Extracts property definitions from the Gene Ontology (GO) Causal Activity Models
 Extracts property definitions from the Relations Ontology (RO).
 
 - **Input**: `data/raw/ro.owl` (OWL format ontology file)
-- **Output**: `data/derived/properties.ro.csv` - Extracted properties with entity IDs, labels, and descriptions
-- **Process**: Parses the RO OWL file and extracts object properties with their labels and descriptions
+- **Output**: `data/derived/properties.ro.csv` - Extracted properties with entity IDs, labels, descriptions, declared `inverse_entity_id` and `is_symmetric`
+- **Process**: Parses the RO OWL file and extracts object properties with their labels and descriptions. A property is symmetric when RO declares `owl:SymmetricProperty` or defines it by the chain `inverse(P) ∘ P`
 
 ### `merge_properties.py`
 
@@ -71,11 +79,72 @@ Merges properties from multiple sources (RO, GO, and custom properties) into a u
   - Removes duplicates, prioritizing entries with descriptions
   - Only creates a new version if entity IDs have changed
 
+### `derive_inverse_pairs.py`
+
+Links converse pairs across the whole KB and elects a canonical member for each, producing
+the KB the gold pipeline consumes. Which KB files are curated, which are generated, and
+what to re-run after an edit: [`data/README.md`](../data/README.md) (with a diagram).
+
+- **Input**: `data/derived/properties.synthesis.2.inverse.csv` (the merged KB plus declared
+  `owl:inverseOf`)
+- **Outputs**: `data/derived/properties.synthesis.2.pairs.csv`, plus
+  `<output>.pending_review.csv` listing pairs awaiting a curator verdict
+- **Rules**, recorded per row in `inverse_source`: `ro` (declared `owl:inverseOf`,
+  authoritative), `has_of` (`has X` ↔ `X of`), `passive_by` (`X-ed by` ↔ the active lemma)
+  and `passive_prep` (the same for the remaining prepositions). Only the first is a fact
+  about the ontology; the other three are proposals marked `needs_review`.
+- **Symmetric relations**: `is_symmetric` is joined from `properties.ro.csv`
+  (`--ro-csv-path`), plus `data/curated/symmetric.csv` (`--symmetric-csv`) for entries RO
+  does not cover. A symmetric relation has no converse, so the surface-form rules never
+  pair it.
+- **Canonical member**: the KB carries both members of a pair, so a passive mention would
+  otherwise have two equally valid encodings. One member is elected canonical
+  (`is_canonical`, `canonical_entity_id`), and orientation moves into the gold's
+  `direction` field.
+- **Review loop**: fill `verdict` (`accept` / `reject`) in the pending sheet and pass it
+  as `--review-csv`. Rejecting unlinks the pair so each member is its own canonical form
+  again. Review before annotating — a wrong pair merges two distinct relations onto one
+  id, and prompts, gold and scores then all agree with the mistake.
+
+```bash
+uv run python run/preprocessing/derive_inverse_pairs.py \
+  --kb-csv-path data/derived/properties.synthesis.2.inverse.csv \
+  --output-path data/derived/properties.synthesis.2.pairs.csv \
+  --review-csv data/curated/inverse_pairs.review.csv \
+  --ro-csv-path data/derived/properties.ro.csv \
+  --symmetric-csv data/curated/symmetric.csv
+```
+
+## Gold evaluation (`eval/`)
+
+The clustering metrics score the manifold against the pipeline's own weak labels; the gold
+set is the independent track. Full walkthrough: **[Gold
+evaluation](https://growgraph.github.io/pelinker/user_guide/evaluation/)** (source:
+`docs/user_guide/evaluation.md`). Needs `uv sync --extra dev --extra eval` and a provider
+credential.
+
+| Step | Script | Produces |
+|------|--------|----------|
+| 0 | `preprocessing/derive_inverse_pairs.py` | the canonical pairs KB |
+| 1 | `eval/sample_gold.py` (`--extend` to grow) | `sample_manifest.csv`, `sample_texts.jsonl` (roles: `primary` / `double` / `reserve`), `sampling_report.batch<k>.json` |
+| 1a | `eval/audit_sample.py` | `sample_audit.json`: eligibility re-check, empty documents, label coverage per batch |
+| 2 | `eval/annotate_llm.py` | `gold.llm-a.json` + a rejection report |
+| 3 | `eval/annotate_llm.py` with another model family, `--roles primary,double` | `gold.llm-b.json` |
+| 4 | `eval/gold_review_sheet.py` `agreement` / `export` → adjudicate → `import` | κ, `review.tsv`, `gold.verified.json` |
+| 5 | `eval/run_baselines.py` | `baseline_results.{json,csv}` |
+| 6 | `eval/dataset_stats.py` | `dataset_stats.json` |
+
+Two invariants hold throughout: every step consumes the **pairs** KB (a KB without
+`is_canonical` is refused rather than tolerated), and ids are compared in canonical space
+on both sides, so a converse-member answer counts as the relation it names. Point
+`--report-dir` outside the repository — measured numbers belong with the measurement
+writeup.
+
 ## Embedding Scripts
 
 ### `embed_kb_corpus.py`
 
-Embeds a knowledge base corpus using the same pipeline as **stage (A)** of `pelinker.cli.fit` (both call `pelinker.embedder.embed_kb_corpus`).
+Embeds a knowledge base corpus using the same pipeline as **stage (A)** of `pelinker.cli.fit` (both call `pelinker.embed.corpus.embed_kb_corpus`).
 
 - **Purpose**: Stream a text table, find KB property mentions, and write **mention-level** rows (with vectors) to Parquet.
 - **Inputs**:
@@ -95,7 +164,7 @@ Embeds a knowledge base corpus using the same pipeline as **stage (A)** of `peli
 | `--use-gpu` | `false` | Move encoder inference to CUDA (if available). | Use this for speed on large corpora. |
 | `--input-buffer-rows` | `1000` | Rows per pandas chunk when reading the text table. | I/O chunking only; does not control model forward memory. |
 | `--encoder-batch-size` | `200` | Number of table rows encoded per transformer forward pass. | Primary OOM control knob; lower when GPU runs out of memory. |
-| `--nlp-model` | `en_core_web_trf` | spaCy pipeline used for tokenization/lemma processing around mention extraction. | Ensure the model is installed in the `uv` env. |
+| `--nlp-model` | `en_core_web_lg` | spaCy pipeline used for tokenization/lemma processing around mention extraction. | Ensure the model is installed in the `uv` env. |
 | `--max-input-buffers` | *(unset)* | Stop after this many read chunks (`input_buffer_rows` each, except final partial chunk). | Useful for smoke tests without scanning the full corpus. |
 | `--negatives-per-positive` | `0.0` | Number of synthetic negative mentions sampled per positive mention. | `0` disables negatives; `1.0` means roughly one negative per positive. |
 | `--negative-label` | `__NEGATIVE__` | Label assigned to sampled negative rows. | Keep this distinct from all real KB labels. |
@@ -110,17 +179,35 @@ Embeds a knowledge base corpus using the same pipeline as **stage (A)** of `peli
 
 ### `loop.embed.kb.corpus.sh` / `loop.fit.sh`
 
-- **`loop.embed.kb.corpus.sh`**: loops over the same default **`model_type` × `layers_spec`** grid and runs **`embed_kb_corpus.py`** only (Parquet per combo).
+- **`loop.embed.kb.corpus.sh`**: loops over the same default **`model_type` × `layers_spec`** grid and runs **`embed_kb_corpus.py`** only (Parquet per combo). Run it with `bash`. It **refuses to overwrite**: if any target `res_<model>_<layer>.parquet` exists, it stops before embedding anything, so an earlier grid (for example the "before" arm of a weak-label change) is never replaced. Write each grid to a new `--output-parquet-path` directory. Optional flags:
+  - `--models "pubmedbert scibert"` and `--layers "1 2"` narrow the grid;
+  - `--max-input-buffers N` caps the input for a smoke run;
+  - `--no-gpu` runs on CPU.
+
+  It warns when `--kb-csv-path` has no `is_symmetric` column (pass the pairs KB). Check a smoke parquet with `analysis/weak_label_check.py` before launching the full grid.
 - **`loop.fit.sh`**: same grid, but runs **`uv run pelinker-fit`** per combo—**stage (A)** writes `res_<model>_<layer_tag>.parquet` under **`--output-parquet-prefix`**, **stage (B)** writes **`pelinker.<model>.<layer_tag>.gz`** under **`--output-model-prefix`** (`layer_tag` is `layers_spec` with commas replaced by `_` for filenames). Requires four flags: `--input-text-table-path`, `--kb-csv-path`, `--output-parquet-prefix`, **`--output-model-prefix`**. Optional **`--layers`**: **`layers_spec`** list (default `1,2,3`). Comma separates distinct specs; use **semicolons** when one spec contains commas, e.g. `--layers 1,2,3`, `--layers 1`, or `--layers '1,2;3'` (runs `1,2` then `3`).
 
 ## Fitting the linker model
 
 Module: **`pelinker.cli.fit`**. It runs the linker training pipeline in **two conceptual stages**:
 
-1. **Stage (A)** — `embed_kb_corpus(...)` (same function as `run/embed_kb_corpus.py`) when **`input_text_table_path`** is set: **`kb_path`** + text table → **`embeddings_parquet`**.
-2. **Stage (B)** — `Linker.fit(...)` on that Parquet: fusion / negative screener / PCA / UMAP / HDBSCAN at a fixed `min_cluster_size` → fitted linker; serialized via `Linker.dump` (joblib at **`{output_path}.gz`**; the `.gz` suffix is appended automatically). Choose `min_cluster_size` upstream (e.g. `pelinker.model_selection`); this CLI does not run a grid search during fit. It can, however, **read** the upstream choice: pass `selection_report=` for the search winner, or `scale_curve_path=` to extrapolate `min_cluster_size` to this fit's realized row count. In `compact` mode the fit also holds out a `pmid`-grouped slice, scores the MLP entity head against its HDBSCAN teacher, and writes the result to the fit report as `distillation_fidelity`.
+1. **Stage (A)** — `embed_kb_corpus(...)` (same function as `run/embed_kb_corpus.py`) when **`input_text_table_path`** is set: **`kb_path`** + text table → **`embeddings_parquet`**. Verb-predicate labels are matched from the dependency parse, one label per verb mention chosen by voice (`pelinker/text/predicates.py`); other labels match lemma windows. Each row records `direction` and `surface_rule`. Pass the pairs KB (`properties.synthesis.2.pairs.csv`) so symmetric relations are never given an inverse direction.
+2. **Stage (B)** — `Linker.fit(...)` on that Parquet: fusion / negative screener / PCA / UMAP / HDBSCAN at a fixed `min_cluster_size` → fitted linker. Under the default `class_view=reldir`, the catalog's composition is read between canonical relations and every cluster gets a `dominant_direction`, which `predict` and `/link` emit as `direction_predicted`. The linker is serialized via `Linker.dump` (joblib at **`{model_path}.gz`**; the `.gz` suffix is appended automatically). Choose `min_cluster_size` upstream (e.g. `pelinker-model-selection`); this CLI does not run a grid search during fit. It can, however, **read** the upstream choice: pass `selection_report=` for the search winner, or `scale_curve_path=` to extrapolate `min_cluster_size` to this fit's realized row count. In `compact` mode the fit also holds out a `pmid`-grouped slice, scores the MLP entity head against its HDBSCAN teacher, and writes the result to the fit report as `distillation_fidelity`.
+
+**Which stages run is set by `pipeline=`, not inferred from the other options:**
+
+| `pipeline` | Stage (A) | Stage (B) | Use when |
+|------------|-----------|-----------|----------|
+| `embed_only` (**default**) | yes | no | producing a mention parquet to search over |
+| `fit_only` | no | yes | the parquet already exists; passing a text table here is an error |
+| `both` | yes | yes | a single end-to-end train |
+| `auto` | if a text table is given | yes | scripted runs where the text table may or may not be set |
 
-If you omit **`input_text_table_path`**, only **stage (B)** runs (you must already have **`embeddings_parquet`** on disk, e.g. from a prior `embed_kb_corpus.py` run).
+The default is `embed_only`, so a command that sets `input_text_table_path`, `model_path`
+and everything else still writes only the parquet unless you ask for `pipeline=both`.
+
+There are **no implicit path fallbacks**: `model_path` and `report_path` are required for
+any pipeline that fits, and the process fails rather than writing to a default location.
 
 **How to run** (use `uv` so dependencies match `uv.lock`):
 
@@ -144,7 +231,7 @@ Hydra’s **`hydra.output_subdir`** defaults to **`null`** here (no `.hydra` fol
 
 - **`kb_path`**: Property KB CSV. Must include **`label`** and **`entity_id`** (labels are corpus patterns; IDs map fused properties to linker vocabulary—the same schema as **`--kb-csv-path`** for embedding).
 - **`embeddings_parquet`**: Mention-level **Parquet** path—**output** of stage (A) and **input** of stage (B). For stage (B) only, it must already exist and match **`model_type`** / **`layers_spec`**.
-- **`input_text_table_path`** (optional): If set, stage (A) runs and **writes** **`embeddings_parquet`** via `embed_kb_corpus`. If omitted, stage (B) **reads** the existing file.
+- **`input_text_table_path`**: required by `embed_only` and `both`, and **rejected** by `fit_only`. When stage (A) runs it **writes** `embeddings_parquet`; otherwise stage (B) reads it.
 
 ### Optional parameters (defaults)
 
@@ -152,8 +239,15 @@ Hydra’s **`hydra.output_subdir`** defaults to **`null`** here (no `.hydra` fol
 |----------|---------|---------|
 | `model_type` | `pubmedbert` | Embedding backbone (same vocabulary as `embed_kb_corpus.py` / `EmbeddingModelMetadata`). |
 | `layers_spec` | `1` | Which layers to use (string parsed by `str2layers`; e.g. comma-separated indices). |
-| `pca_components` | `100` | PCA dimensionality before UMAP. |
-| `umap_dim` | `8` | UMAP output dimension for clustering. |
+| `pipeline` | `embed_only` | Which stages run: `embed_only`, `fit_only`, `both`, `auto`. |
+| `pca_components` | *(unset)* | PCA dimensionality before UMAP; unset takes `selection_report`'s value, else 100. |
+| `umap_dim` | *(unset)* | UMAP output dimension for clustering; unset takes `selection_report`'s value, else 8. |
+| `predict_mode` | `compact` | `compact` (ParametricUMAP + MLP entity head) or `legacy` (UMAP + HDBSCAN `approximate_predict`). |
+| `screener_kind` | `lda` | Negative screener: `lda` or `svm`; persisted on the artifact. |
+| `projection_enabled` | `true` | When false, skip the 3D manifold OOV score model, removing that predict-time gate. |
+| `model_types` / `layers_specs` | *(unset)* | Per-parquet backbone and layers when fusing several parquets; length 1 broadcasts. Unset, the scalars apply unless the parquet stem matches `..._<model>_<layers>`. |
+| `cluster_viz_method` | `pca` | Projection used for the cluster visualization: `pca` or `umap`. |
+| `clustering_sample_index` | `0` | Bootstrap index for the clustering subsample; match model selection's `sample_idx` to reproduce its draw. |
 | `clustering_sample_rows` | *(unset)* | Max mention rows per clustering bootstrap draw (stratified). Omit to use all loaded rows after filters. |
 | `seed` | `13` | Bootstrap seed for clustering subsample draws; default for `mention_cap_seed` and `screener_seed`. |
 | `pca_seed` | `13` | Random seed for PCA and cluster-viz PCA. |
@@ -163,6 +257,8 @@ Hydra’s **`hydra.output_subdir`** defaults to **`null`** here (no `.hydra` fol
 | `max_mentions_per_entity` | *(unset)* | Optional seeded cap on mention rows per KB entity before subsampling. |
 | `max_mentions_negative` | *(unset)* | Optional cap on synthetic negative rows; omit to leave negatives uncapped. |
 | `mention_cap_seed` | `seed` | RNG seed for per-entity mention cap (defaults to `seed`). |
+| **`class_view`** | `reldir` | Classes the fit's ARI and catalog composition use (`pelinker/kb/classes.py`): `raw` (matched label), `rel` (canonical relation) or `reldir` (canonical relation + direction). `rel` / `reldir` need the pairs KB; `raw` reproduces earlier fits. See [Class views](#class-views). |
+| `class_kb_path` | `kb_path` | Pairs KB the class view is computed from. |
 | `min_cluster_size` | *(unset)* | HDBSCAN `min_cluster_size`. Omit to take it from `selection_report`, else `scale_curve_path`, else `20`. An explicit value always wins; the origin is recorded in the fit report under `min_cluster_size_provenance`. |
 | **`selection_report`** | *(unset)* | `selected_hyperparameters.json` (or the report dir holding it) from `pelinker-model-selection` / `pelinker-dim-selection`. Fills in `pca_components`, `umap_dim`, `umap_n_neighbors`, `min_cluster_size` when those are not set here — so the search's winner reaches the fit instead of being retyped. |
 | **`scale_curve_path`** | *(unset)* | `scale_curve.json` from `pelinker-scale-curve`. When set (and `min_cluster_size` is not), the hyperparameter is extrapolated to this fit's realized manifold row count rather than transferred verbatim from the selection sample size. |
@@ -171,12 +267,14 @@ Hydra’s **`hydra.output_subdir`** defaults to **`null`** here (no `.hydra` fol
 | `entity_head_holdout_group_col` | `pmid` | Column kept whole across the holdout split; mentions from one document are correlated, so a row-level split inflates measured agreement. |
 | `distillation_gates_enabled` | `true` | Check the fitted head against `distillation_min_entity_agreement` (0.95) and `distillation_max_emit_rate_rel_delta` (0.10). |
 | `distillation_on_failure` | `warn` | `warn` keeps the model and records the breach in the report; `raise` aborts the fit. |
-| `output_path` | *(see below)* | Where `linker.dump` writes the artifact. |
+| **`model_path`** | *(required to fit)* | Base path `Linker.dump` writes to; `.gz` is appended for you. |
+| **`report_path`** | *(required to fit)* | Directory for `linker_fit.clustering_report.json.gz`, `linker_fit.cluster_composition.json.gz` and `linker_fit.kb_out.json`. |
+| `entity_head_hidden_layers` | `[256, 128, 128]` | MLP hidden sizes for `compact` mode. |
 | `use_gpu` | `false` | GPU for transformer encoding when embedding the corpus. |
 | `input_buffer_rows` | `1000` | Stage (A): rows per pandas read pass over the text table (I/O buffer; does **not** control GPU memory). |
 | `encoder_batch_size` | `200` | Stage (A): table rows per encoder forward pass—**lower this if the GPU runs out of memory**. |
-| `batch_size` | `1000` | Stage (B): rows per batch when **reading large embedding parquet files**; same role as `model_selection.py --batch-size`. |
-| `nlp_model` | `en_core_web_trf` | spaCy pipeline for mention extraction (`uv run spacy download en_core_web_trf`). |
+| `batch_size` | `1000` | Stage (B): rows per batch when **reading large embedding parquet files**; same role as `pelinker-model-selection --batch-size`. |
+| `nlp_model` | `en_core_web_lg` | spaCy pipeline for mention extraction (pinned by the `dev` extra). |
 | `max_input_buffers` | *(unset)* | Stage (A): stop after this many text-table read passes (each up to `input_buffer_rows` rows); unrelated to `encoder_batch_size`. |
 | **`kb_name`** | stem of `kb_path` | Display name stored in `KBConfig`. |
 | **`kb_version`** | `0.1.0` | KB version string stored on the model. |
@@ -184,44 +282,83 @@ Hydra’s **`hydra.output_subdir`** defaults to **`null`** here (no `.hydra` fol
 | **`kb_description`** | `""` | Free-form KB description. |
 | **`kb_entity_count`** | *(unset)* | Optional; if omitted, may be filled from the fitted vocabulary in `KBConfig`. |
 
-**Default output location**: if `output_path` is not set, the model is written under the `pelinker.store` package resources as `pelinker.model.{model_type}.{layers_str}` (e.g. `pelinker.model.pubmedbert.1` → **`pelinker.model.pubmedbert.1.gz`** next to that package resource). Set `output_path` to an explicit filesystem path (without adding `.gz` yourself) for reproducible artifacts.
+**There is no default output location.** A pipeline that fits requires both `model_path` and `report_path`, and fails rather than writing somewhere implicit. Give `model_path` without the `.gz` suffix — the linker appends it. A fit also refuses to overwrite: `both` and `embed_only` abort when a target parquet already exists.
 
 **Migration (sample size):** `frac` / `eval_max_rows` / `n_embedding_batches` were replaced by `clustering_sample_rows` (absolute cap after load filters). Example: `frac=0.1` on 1M rows ≈ `clustering_sample_rows=100000`. Old `n_embedding_batches=50` with `batch_size=1000` truncated parquet reads before filters; use `clustering_sample_rows=50000` after filters instead.
 
 ### Examples
 
-Embed a corpus from the synthesized KB and save to a known path:
+End-to-end: embed a corpus and fit a linker in one run (note `pipeline=both` — the default embeds only):
+
+```bash
+uv run pelinker-fit \
+  pipeline=both \
+  kb_path=data/derived/properties.synthesis.2.pairs.csv \
+  input_text_table_path=<corpus>.tsv.gz \
+  embeddings_parquet=<workdir>/corpus_pubmedbert_1.parquet \
+  model_path=<models>/pelinker.pubmedbert.run1 \
+  report_path=<workdir>/reports/run1
+```
+
+Embed only, to produce a parquet for the hyperparameter searches:
 
 ```bash
 uv run pelinker-fit \
-  kb_path=data/derived/properties.synthesis.1.csv \
-  input_text_table_path=data/corpus/articles.tsv.gz \
-  embeddings_parquet=outputs/corpus_pubmedbert_1.parquet \
-  output_path=models/pelinker.pubmedbert.run1
+  kb_path=data/derived/properties.synthesis.2.pairs.csv \
+  input_text_table_path=<corpus>.tsv.gz \
+  embeddings_parquet=<workdir>/res_pubmedbert_1.parquet
 ```
 
-Reuse parquet output from a prior `embed_kb_corpus.py` run and tune UMAP:
+Fit from an existing parquet, taking the search winner rather than retyping it:
 
 ```bash
 uv run python -m pelinker.cli.fit \
-  kb_path=data/derived/properties.synthesis.1.csv \
-  embeddings_parquet=outputs/res_pubmedbert_1.parquet \
-  umap_dim=12 \
-  output_path=models/pelinker.from_parquet
+  pipeline=fit_only \
+  kb_path=data/derived/properties.synthesis.2.pairs.csv \
+  embeddings_parquet=<workdir>/res_pubmedbert_1.parquet \
+  selection_report=<workdir>/reports/dim_selection_2 \
+  model_path=<models>/pelinker.from_parquet \
+  report_path=<workdir>/reports/from_parquet
 ```
 
 Short GPU smoke test truncating stage (A) after two table read passes (`input_buffer_rows` rows each unless the file ends sooner):
 
 ```bash
 uv run pelinker-fit \
-  kb_path=data/derived/properties.synthesis.1.csv \
-  input_text_table_path=data/corpus/articles.tsv.gz \
-  embeddings_parquet=outputs/corpus_smoke_trunc.parquet \
+  kb_path=data/derived/properties.synthesis.2.pairs.csv \
+  input_text_table_path=<corpus>.tsv.gz \
+  embeddings_parquet=<workdir>/corpus_smoke_trunc.parquet \
   max_input_buffers=2 \
   input_buffer_rows=500 \
   use_gpu=true
 ```
 
+## Class views
+
+The KB is used as given; its entries and labels are never edited. What the objective and
+the catalog count as one class is a *view* of it, computed from the pairs KB's own
+declarations (`pelinker/kb/classes.py`):
+
+| View | Class of a mention | Use |
+|---|---|---|
+| `raw` | the matched label | reproduces fits and searches from before views existed |
+| `rel` | the canonical relation | catalog composition (merges between relations) |
+| `reldir` | canonical relation + direction relative to it | **default** for the ARI of every search and of the fit |
+
+**Why `reldir` is the default.** The weak-label matcher prefers the label whose own reading
+has the mention's voice. A passive mention of a relation with a converse entry is
+therefore stored under that entry, while a passive mention of a relation without one is
+stored under the active label with `direction=inverse`. Against raw labels, ARI rewards
+separating voices for the first kind of relation and penalizes it for the second.
+`reldir` makes every passive an inverse class. `rel` would fold the voices together and
+reward clusters that merge them, which removes the only direction signal an embedding-only
+linker has.
+
+The selection CLIs take `--class-view` and `--class-kb-path`, and the fit takes
+`class_view=` and `class_kb_path=` (defaulting to `kb_path`). A view enters the search
+checkpoint fingerprint, with the KB by content, so a run cannot resume under a different
+view or a re-derived KB.
+
 ## Batch linking (`pelinker-link-files`)
 
 Module: **`pelinker.cli.link_files`**. Console script: **`uv run pelinker-link-files`** (same as `uv run python -m pelinker.cli.link_files`).
@@ -240,7 +377,7 @@ Runs **`Linker.predict`** on one or more UTF-8 inputs (plain text = one document
 
 ## HTTP server smoke tests
 
-After you have a dumped linker (packaged default or `output_path` from fit), you can run the **FastAPI** server from **`pelinker.cli.server`**. Configuration uses Hydra like fit; defaults live in `pelinker/conf/server.yaml`.
+After you have a dumped linker (the packaged default, or `model_path` from a fit), you can run the **FastAPI** server from **`pelinker.cli.server`**. Configuration uses Hydra like fit; defaults live in `pelinker/conf/server.yaml`.
 
 **Start the server** (pick one):
 
@@ -249,29 +386,29 @@ After you have a dumped linker (packaged default or `output_path` from fit), you
 
 Common Hydra overrides: `host`, `port` (default **8599**), `model_file_spec` (linker dump **without** the `.gz` suffix—same rule as `Linker.load`), `thr_score`, `use_gpu`, `cors_allow_origins`. API routes include `GET /health`, `GET /info`, `GET /model`, `POST /link`, and `POST /link/debug`; interactive docs are at **`/docs`** when the server is up.
 
-### `test_server.py`
+### `smoke_server.py`
 
 Small **Click** client in this directory to hit those routes while developing. Start the server in one terminal, then:
 
 ```bash
-uv run python run/test_server.py --endpoint health
-uv run python run/test_server.py --endpoint info
-uv run python run/test_server.py --endpoint model
-uv run python run/test_server.py --endpoint link
-uv run python run/test_server.py --endpoint link-debug
+uv run python run/smoke_server.py --endpoint health
+uv run python run/smoke_server.py --endpoint info
+uv run python run/smoke_server.py --endpoint model
+uv run python run/smoke_server.py --endpoint link
+uv run python run/smoke_server.py --endpoint link-debug
 ```
 
-Use **`--host`** / **`--port`** so they match the running server (defaults: `localhost` and **8599**). For **`link`** and **`link-debug`**, omit **`--input-path`** to send a built-in two-document `texts` example, or pass a JSON file whose root is an object with **`text`** or **`texts`** (optional keys such as `thr_score`, `use_gpu`, `max_length`; for debug, `include_entity_anomaly_metrics`, `kb_validation`). Plain or **`.json.gz`** files are accepted (via `pelinker.io.load_json_path`). Use **`--output`** to write the JSON response to a file instead of printing; **`--timeout`** defaults to 300 seconds for slow cold starts.
+Use **`--host`** / **`--port`** so they match the running server (defaults: `localhost` and **8599**). For **`link`** and **`link-debug`**, omit **`--input-path`** to send a built-in two-document `texts` example, or pass a JSON file whose root is an object with **`text`** or **`texts`** (optional keys such as `thr_score`, `use_gpu`, `max_length`; for debug, `include_entity_anomaly_metrics`, `kb_validation`). Plain or **`.json.gz`** files are accepted (via `pelinker.data.load_json_path`). Use **`--output`** to write the JSON response to a file instead of printing; **`--timeout`** defaults to 300 seconds for slow cold starts.
 
 ## Analysis Scripts
 
 Scripts in the `analysis/` directory evaluate embedding quality and select diverse entities.
 
-### `model_selection.py`
+### `pelinker-model-selection`
 
-Implementation: [`pelinker.model_selection`](../../pelinker/model_selection/) (this script is a thin shim).
+Implementation: [`pelinker.search.model_selection`](../pelinker/search/model_selection/); CLI `pelinker/cli/model_selection.py`.
 
-Measures the quality of embeddings obtained from `embed_kb_corpus.py` by evaluating clustering performance.
+Measures the quality of embeddings obtained from stage (A) by evaluating clustering performance. Required: `--input-dir`, `--report-path`.
 
 - **Purpose**: Evaluates how well embeddings cluster semantically similar properties together
 - **Input**: Directory containing parquet files (pattern: `res_<model>_<layer>.parquet`)
@@ -287,10 +424,11 @@ Measures the quality of embeddings obtained from `embed_kb_corpus.py` by evaluat
   - Supports multiple sampling runs for statistical robustness
   - Shared mention-frame load with `pelinker-fit`: optional `--drop-rare-entities`, `--max-mentions-per-entity`, then `--clustering-sample-rows` (omit = all loaded rows)
   - **Optional**: `--selected-labels-kb-path` parameter to evaluate quality over a specific subset of labels from a selected knowledge base CSV file
+  - `--class-view` (default `reldir`) and `--class-kb-path` set the classes ARI scores against — see [Class views](#class-views). The same two options exist on `pelinker-dim-selection` and `pelinker-scale-curve`
 - **Metrics** (two-level, same as `dim_selection.py`):
   - **MCS** (`min_cluster_size`): HDBSCAN hyperparameter — smallest cluster HDBSCAN will form; searched on an inner grid
-  - **Inner** (choose MCS): `grid_objective=dbcv_ari_mean_minmax` — min–max normalize mean DBCV and mean ARI on the MCS curve, average, smooth, pick the left plateau
-  - **Outer** (rank model×layer): at each combo’s pooled MCS, combine mean DBCV + mean ARI with the same DBCV+ARI pooling (minmax across candidates). Column `best_score` remains mean DBCV (heatmaps); `outer_score` chooses the winner
+  - **Inner** (choose MCS): `grid_objective=dbcv_ari_geomean` — clip mean DBCV and mean ARI at 0, take `sqrt(dbcv*ari)` per bootstrap sample, smooth, then pick the largest MCS within one *paired* standard error of the best (`grid_one_se_k`, default 1.0). The geometric mean ranks grid points identically under any rescaling of either metric, so nothing needs normalizing
+  - **Outer** (rank model×layer): at each combo’s pooled MCS, combine mean DBCV + mean ARI with the *same* clipped geometric mean. Each candidate is scored from its own numbers, so adding one cannot reorder the others. Column `best_score` remains mean DBCV (heatmaps); `outer_score` chooses the winner
 
 ### `compact_predict_study.py`
 
@@ -298,16 +436,21 @@ Compares **legacy** (UMAP + HDBSCAN `approximate_predict`) vs **compact** (Param
 
 - **Arms**: A legacy, B compact (shipped), C iso-manifold, D iso-head, E LinearSVC underfit control
 - **Gates**: entity-id agreement vs A ≥ 0.95, emit-rate within ±10%, size ≤ 15 MB or ≥5× smaller, latency ≤ 1.5× A
-- **Split**: 65/15/20 train/tune/holdout, **grouped by `pmid`** via `pelinker.distillation.grouped_holdout_split`. It was a plain row shuffle before, which put mentions of the same document on both sides and made every agreement number optimistic.
+- **Split**: 65/15/20 train/tune/holdout, **grouped by `pmid`** via `pelinker.linker.distillation.grouped_holdout_split`. It was a plain row shuffle before, which put mentions of the same document on both sides and made every agreement number optimistic.
 - **Outputs**: `arms.csv`, `summary.json` with explicit pass/fail under `--report-dir`
 - **Example**: `uv run python run/analysis/compact_predict_study.py --embeddings-parquet … --report-dir …`
-- **First real-data run is committed** at [`reports/compact_predict_study/`](../../reports/compact_predict_study/). The shipped compact default **fails** the agreement gate (0.829 vs 0.95), and the ablation places the cost in the ParametricUMAP manifold rather than the MLP head (arm D, standard UMAP + MLP, scores 0.998). Read the README there before changing `predict_mode`.
+- **Run it before trusting `predict_mode=compact` on your data.** The arms are built so a
+  failure is attributable: C and D vary the manifold and the head one at a time, so a gap
+  between the shipped compact path and legacy can be charged to the ParametricUMAP manifold
+  or to the MLP entity head rather than to "compact mode" as a whole, and E is an underfit
+  control that should *not* pass. Reports land under `--report-dir`, which is gitignored —
+  keep the numbers with the measurement writeup, not in this repo.
 
 For a per-fit number rather than this five-arm audit, `pelinker-fit` now measures held-out student-vs-teacher fidelity on **every** compact fit and writes a `distillation_fidelity` block into `linker_fit.clustering_report.json.gz` — see [Fitting the linker model](#fitting-the-linker-model).
 
 ### `pelinker-scale-curve`
 
-Implementation: [`pelinker.scale_curve`](../../pelinker/scale_curve/); CLI `pelinker/cli/scale_curve.py`.
+Implementation: [`pelinker.search.scale_curve`](../pelinker/search/scale_curve/); CLI `pelinker/cli/scale_curve.py`.
 
 Measures how the chosen `min_cluster_size` moves with the mention-frame size, instead of transferring an integer picked on a subsample straight into a full-corpus fit.
 
@@ -319,17 +462,18 @@ Measures how the chosen `min_cluster_size` moves with the mention-frame size, in
 
 ```bash
 uv run pelinker-scale-curve \
-  --input-parquet /home/alexander/data/pelinker/experiment.d/res_pubmedbert_2.parquet \
-  --report-path reports/scale_curve_2 \
+  --input-parquet <workdir>/res_pubmedbert_2.parquet \
+  --report-path <workdir>/reports/scale_curve_2 \
+  --class-kb-path data/derived/properties.synthesis.2.pairs.csv \
   --rungs 10000,25000,50000,100000 \
   --pca-components 22 --umap-dim 3 --n-sample 3
 ```
 
-### `dim_selection.py`
+### `pelinker-dim-selection`
 
-Implementation: [`pelinker.dim_selection`](../../pelinker/dim_selection/) (shim: `run/analysis/dim_selection.py` → `pelinker.cli.dim_selection`).
+Implementation: [`pelinker.search.dim_selection`](../pelinker/search/dim_selection/); CLI `pelinker/cli/dim_selection.py`.
 
-After model selection picks a winning embedding combo, search **`(pca_components, umap_dim)`** on that single parquet with the same clustering metrics stack.
+After model selection picks a winning embedding combo, search **`(pca_components, umap_dim)`** on that single parquet with the same clustering metrics stack. Required: `--input-parquet`, `--report-path`.
 
 - **Purpose**: Choose robust PCA and UMAP dimensions for the transform pipeline (defaults today: 100 and 8)
 - **Input**: One mention-level parquet (`--input-parquet`); model/layer parsed from the filename (or `--model` / `--layer`)
@@ -349,8 +493,9 @@ After model selection picks a winning embedding combo, search **`(pca_components
 
 ```bash
 uv run python -m pelinker.cli.dim_selection \
-  --input-parquet /home/alexander/data/pelinker/experiment.d/res_pubmedbert_2.parquet \
-  --report-path reports/dim_selection_2 \
+  --input-parquet <workdir>/res_pubmedbert_2.parquet \
+  --report-path <workdir>/reports/dim_selection_2 \
+  --class-kb-path data/derived/properties.synthesis.2.pairs.csv \
   --n-sample 3 \
   --clustering-sample-rows 10000
 ```
@@ -380,12 +525,82 @@ Publication-style **figures** for pre-classifier anomaly space: compares trainin
 - **Run**: `uv run python run/analysis/oov_analysis.py --help` for the full CLI (composite “paper” figure, alignment with the negative screener, etc.).
 - **Dependencies**: Uses **`matplotlib`** / **`seaborn`**; install optional **dev** extras if needed (`uv sync --extra dev` so the plotting stack matches `pyproject.toml`).
 
-### `replot_dbcv_ari_scatter.py`
+### `pelinker-replot`
+
+Regenerates the model-selection figures — including the **DBCV vs ARI** scatter — from the
+artifacts already under a report directory, with no re-embedding and no re-clustering.
+
+```bash
+uv run pelinker-replot <report-dir>
+```
+
+Takes the report directory as its one positional argument. Optional: `--checkpoint`,
+`--grid-one-se-k` (re-solve the grid with a different one-SE width),
+`--grid-cluster-count-reward`, `--grid-n-entities`, `--all-pca-pairgrid-samples`.
+
+### `replot_fit.py`
+
+Visualises cluster composition from a **fit** report (as opposed to a selection report):
+composition bars and pies, an interactive cluster view, and an entity→cluster Sankey.
+
+```bash
+uv run python run/analysis/replot_fit.py <report-dir>
+```
+
+Reads `linker_fit.clustering_report.json.gz`, `linker_fit.cluster_composition.json.gz` and
+`linker_fit.kb_out.json`. Optional: `--top-n` (3), `--max-clusters`, `--max-entities`,
+`--pmid-text-table`, `--sankey-min-frac` (0.0), `--cluster-label` (`display` | `short`),
+`--viz-all-kb`, `--show`.
+
+### `cluster_stability.py`
+
+Asks whether clusters are the same *objects* from one bootstrap draw to the next, rather
+than merely the same in number — a count can be stable while membership churns.
+
+```bash
+uv run python run/analysis/cluster_stability.py \
+  --labels-parquet <report-dir>/sample_cluster_labels.parquet \
+  --report-dir <workdir>/stability
+```
+
+The input comes from `pelinker-dim-selection --persist-labels` (off by default). Writes
+`stability_summary.json`, `stability_pairs.csv` and `stability_per_cluster.csv`; optional
+`--jaccard-floor` (0.5) sets what counts as the same cluster across draws.
+
+### `weak_label_check.py`
+
+Acceptance check for the weak labels in a stage-(A) mention parquet. Run it on a smoke
+run before a full embedding grid. It reports how labels were assigned (`surface_rule`,
+`direction`) and fails, with a non-zero exit, on any of the following:
+- a span carrying two labels;
+- a labelled span nested inside another;
+- a span labelled with both a relation and its converse;
+- a symmetric relation with `direction=inverse`;
+- a label the KB does not hold (the class views refuse it).
+
+For a pairs KB it also reports the number of classes each view yields and the mention
+mass by direction relative to the canonical relation; `classes_reldir` is the number of
+classes the selection objective scores against.
+
+```bash
+uv run python run/analysis/weak_label_check.py \
+  --parquet <dir>/res_pubmedbert_1.parquet \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv
+```
+
+### `direction_diagnostic.py`
 
-Rebuilds the **DBCV vs ARI** scatter PNG from an existing **`results_grid_per_sample.csv`** produced by `model_selection.py` (no re-embedding).
+Sizes the directionality problem before any model is built: how far converse pairs collide
+in the learned space, reported as collision rate, co-membership and separation over the
+KB's declared pairs.
 
 ```bash
-uv run python run/analysis/replot.py path/to/results_grid_per_sample.csv
+uv run python run/analysis/direction_diagnostic.py \
+  --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+  --model-path <models>/pelinker.pubmedbert.run1 \
+  --report-dir <workdir>/direction
 ```
 
-Optional **`-o` / `--output`** overrides the default path next to the CSV (`model.dbcv_vs_ari.png`).
+Takes exactly one source of assignments: `--model-path`, `--fit-report` or
+`--assignments-parquet`. It deliberately refuses the cluster-composition artifact, which is
+truncated to the top N entities per cluster and would understate collisions.
diff --git a/run/analysis/cluster_stability.py b/run/analysis/cluster_stability.py
new file mode 100644
index 0000000..83a4c31
--- /dev/null
+++ b/run/analysis/cluster_stability.py
@@ -0,0 +1,141 @@
+"""Cluster-identity stability across bootstrap draws.
+
+Consumes ``sample_cluster_labels.parquet`` — written by
+``pelinker-dim-selection --persist-labels`` — and reports whether the clusters are the
+same objects from draw to draw, not merely the same *number* of objects. A stable cluster
+count with churning membership is indistinguishable from a stable clustering if only the
+count is reported, which is what the submitted evaluation did.
+
+Outputs, under ``--report-dir``:
+
+- ``stability_summary.json`` — cluster-count spread, mean pairwise Jaccard, mean
+  cross-draw ARI, and the fraction of reference clusters that persist above the floor;
+- ``stability_pairs.csv`` — one row per draw pair;
+- ``stability_per_cluster.csv`` — per reference-draw cluster, mean best-match Jaccard and
+  match rate across the other draws.
+
+Rows are identified by ``(pmid, itext, a_abs, b_abs)`` where available, so a document
+that mentions a predicate twice contributes two distinct rows. Numbers are measurement
+output: write them outside this repository.
+
+Usage:
+
+    uv run python run/analysis/cluster_stability.py \
+        --labels-parquet <report>/sample_cluster_labels.parquet \
+        --report-dir <workdir>/eval-runs/stability
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+
+import click
+import pandas as pd
+
+from pelinker.clustering.stability import analyze_stability
+from pelinker.core.paths import ExpandedPath
+
+logger = logging.getLogger(__name__)
+
+_ROW_KEY_COLUMNS = ("pmid", "itext", "a_abs", "b_abs")
+
+
+def row_keys(frame: pd.DataFrame) -> pd.Series:
+    """Stable per-mention identity, falling back to whatever columns exist."""
+    present = [c for c in _ROW_KEY_COLUMNS if c in frame.columns]
+    if not present:
+        raise click.ClickException(
+            f"labels parquet needs at least one of {list(_ROW_KEY_COLUMNS)} to identify "
+            "rows across draws"
+        )
+    keys = frame[present[0]].astype(str)
+    for col in present[1:]:
+        keys = keys + ":" + frame[col].astype(str)
+    return keys
+
+
+def draws_from_labels(
+    frame: pd.DataFrame,
+    *,
+    pca_components: int | None,
+    umap_dim: int | None,
+) -> list[dict[str, int]]:
+    """Split the persisted labels into one ``row_key -> cluster`` mapping per draw."""
+    work = frame
+    for col, value in (("pca_components", pca_components), ("umap_dim", umap_dim)):
+        if value is not None and col in work.columns:
+            work = work.loc[work[col] == value]
+    if work.empty:
+        raise click.ClickException("no rows left after filtering on the requested cell")
+
+    # A single (pca, umap) cell must be compared at a time: clusters from different
+    # dimensionalities are not the same objects and matching them would be meaningless.
+    for col in ("pca_components", "umap_dim"):
+        if col in work.columns and work[col].nunique() > 1:
+            raise click.ClickException(
+                f"labels span several {col} values {sorted(work[col].unique())}; pass "
+                f"--{col.replace('_', '-')} to pick one cell"
+            )
+
+    work = work.assign(_row_key=row_keys(work))
+    draws: list[dict[str, int]] = []
+    for sample_idx, group in work.groupby("sample_idx", sort=True):
+        draws.append(dict(zip(group["_row_key"], group["cluster"].astype(int))))
+        logger.info("draw %s: %d rows", sample_idx, len(group))
+    return draws
+
+
+@click.command()
+@click.option("--labels-parquet", required=True, type=ExpandedPath(exists=True))
+@click.option("--report-dir", required=True, type=ExpandedPath())
+@click.option(
+    "--jaccard-floor",
+    default=0.5,
+    show_default=True,
+    help="Overlap at or above which two clusters count as the same cluster.",
+)
+@click.option("--pca-components", default=None, type=int)
+@click.option("--umap-dim", default=None, type=int)
+@click.option("--reference-index", default=0, show_default=True, type=int)
+def main(
+    labels_parquet: str,
+    report_dir: str,
+    jaccard_floor: float,
+    pca_components: int | None,
+    umap_dim: int | None,
+    reference_index: int,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+
+    frame = pd.read_parquet(labels_parquet)
+    logger.info("Loaded %d label rows from %s", len(frame), labels_parquet)
+    draws = draws_from_labels(frame, pca_components=pca_components, umap_dim=umap_dim)
+    if len(draws) < 2:
+        raise click.ClickException(
+            f"stability needs at least two draws, found {len(draws)} — re-run the "
+            "selection with --persist-labels and n_sample >= 2"
+        )
+
+    report = analyze_stability(
+        draws, jaccard_floor=jaccard_floor, reference_index=reference_index
+    )
+    summary = report.to_jsonable()
+    logger.info("Summary: %s", json.dumps(summary, indent=1))
+
+    out = Path(report_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    (out / "stability_summary.json").write_text(json.dumps(summary, indent=1))
+    pd.DataFrame(list(report.pairs)).to_csv(out / "stability_pairs.csv", index=False)
+    pd.DataFrame(
+        [
+            {"cluster_id": cid, **stats}
+            for cid, stats in sorted(report.per_cluster.items())
+        ]
+    ).to_csv(out / "stability_per_cluster.csv", index=False)
+    logger.info("Wrote stability artifacts to %s", out)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/analysis/compact_predict_study.py b/run/analysis/compact_predict_study.py
index 3f18847..1b8605f 100644
--- a/run/analysis/compact_predict_study.py
+++ b/run/analysis/compact_predict_study.py
@@ -38,20 +38,20 @@
 from hdbscan import approximate_predict
 from sklearn.metrics import adjusted_rand_score
 
-from pelinker.config import TransformConfig
-from pelinker.clustering_fit import fit_manifold_clustering
-from pelinker.distillation import (
+from pelinker.core.config import TransformConfig
+from pelinker.clustering.fit import fit_manifold_clustering
+from pelinker.linker.distillation import (
     cluster_to_entity_map,
     grouped_holdout_split,
     labels_to_entities,
 )
-from pelinker.entity_head import (
+from pelinker.linker.entity_head import (
     EntityHead,
     fit_linear_svc_entity_head,
     fit_mlp_entity_head,
 )
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.transform import (
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.clustering.transform import (
     EmbeddingTransformer,
     is_parametric_umap,
     save_clustering_manifold,
@@ -113,7 +113,7 @@ def _split_idx(
 
     Previously a plain row shuffle, which put mentions of the same document on both
     sides and made every agreement number optimistic. Grouping is delegated to
-    :func:`~pelinker.distillation.grouped_holdout_split` so the study and the in-fit
+    :func:`~pelinker.linker.distillation.grouped_holdout_split` so the study and the in-fit
     measurement leak the same way (i.e. not at all) and stay comparable.
     """
     hold_split = grouped_holdout_split(frame, holdout_fraction=0.20, random_state=seed)
diff --git a/run/analysis/dim_selection.py b/run/analysis/dim_selection.py
deleted file mode 100644
index dfe063a..0000000
--- a/run/analysis/dim_selection.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Thin shim — implementation lives in ``pelinker.cli.dim_selection``."""
-
-from pelinker.cli.dim_selection import main
-
-if __name__ == "__main__":
-    main()
diff --git a/run/analysis/direction_diagnostic.py b/run/analysis/direction_diagnostic.py
new file mode 100644
index 0000000..ded5380
--- /dev/null
+++ b/run/analysis/direction_diagnostic.py
@@ -0,0 +1,241 @@
+"""How badly do converse predicate pairs collide in the learned space?
+
+Sizes the directionality problem *before* any model is built. For every KB inverse pair
+(``owl:inverseOf``, carried by ``run/preprocessing/extract_properties_ro.py``) whose two
+members both appear in the mention frame, the script reports:
+
+- **collision rate** — pairs whose two members' mention mass concentrates in the same
+  cluster, i.e. the clustering cannot tell a predicate from its converse;
+- **co-membership** — per pair, the fraction of shared mention mass in the top common
+  cluster and the Jensen–Shannon-free overlap of their cluster distributions;
+- **separation** — pairs whose members occupy disjoint clusters (the direction signal is
+  already there and only the *labeling* collapses it).
+
+Input is the mention frame with cluster assignments, from any of: a fit's clustering
+report (``--fit-report``, the ``assignments`` block — no refit needed), the training
+frame on a fitted linker (``--model-path``), or a parquet with ``entity`` + ``cluster``
+columns (``--assignments-parquet``). Distinguishing collision from separation matters —
+if collisions are rare, the paper claim only needs scoping; if they dominate, a direction
+stage is load-bearing.
+
+Note the cluster-composition artifact is deliberately *not* accepted as input: it stores
+only the top-N entities per cluster for plotting, so per-entity distributions read off it
+are truncated and would understate overlap.
+
+Numbers are measurement output: write them outside this repository and cite them from
+the measurement writeup.
+
+Usage:
+
+    uv run python run/analysis/direction_diagnostic.py \
+        --model-path <models>/pelinker.pubmedbert.1 \
+        --kb-csv-path data/derived/properties.synthesis.3.csv \
+        --report-dir <workdir>/eval-runs/direction-diagnostic
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+
+import click
+import pandas as pd
+
+from pelinker.clustering.composition import filter_emergent_assignments
+from pelinker.core.paths import ExpandedPath
+
+logger = logging.getLogger(__name__)
+
+
+def load_inverse_pairs(kb: pd.DataFrame) -> list[tuple[str, str]]:
+    """Unordered ``owl:inverseOf`` pairs where both members carry a KB label."""
+    if "inverse_entity_id" not in kb.columns:
+        raise click.ClickException(
+            "KB CSV has no 'inverse_entity_id' column — regenerate it with "
+            "run/preprocessing/extract_properties_ro.py and merge_properties.py"
+        )
+    labelled = kb[kb["label"].notna()]
+    known = set(labelled["entity_id"].astype(str))
+    seen: set[tuple[str, str]] = set()
+    for _, row in labelled.iterrows():
+        inverse = row["inverse_entity_id"]
+        if pd.isna(inverse):
+            continue
+        a, b = str(row["entity_id"]), str(inverse)
+        if b not in known or a == b:
+            continue
+        seen.add((a, b) if a < b else (b, a))
+    return sorted(seen)
+
+
+def cluster_distribution(assignments: pd.DataFrame, label: str) -> dict[int, float]:
+    """Normalized mention mass of one KB label over emergent clusters."""
+    rows = assignments.loc[assignments["entity"].astype(str) == label]
+    if rows.empty:
+        return {}
+    counts = rows["cluster"].astype(int).value_counts()
+    total = float(counts.sum())
+    return {int(c): float(n) / total for c, n in counts.items()}
+
+
+def overlap(dist_a: dict[int, float], dist_b: dict[int, float]) -> float:
+    """Bhattacharyya-free overlap: total shared probability mass over clusters."""
+    return sum(
+        min(dist_a.get(c, 0.0), dist_b.get(c, 0.0)) for c in set(dist_a) | set(dist_b)
+    )
+
+
+def diagnose(
+    assignments: pd.DataFrame,
+    pairs: list[tuple[str, str]],
+    labels: dict[str, str],
+    *,
+    collision_threshold: float,
+) -> tuple[list[dict], dict]:
+    """Per-pair collision measures plus the aggregate summary."""
+    rows: list[dict] = []
+    for id_a, id_b in pairs:
+        label_a, label_b = labels.get(id_a), labels.get(id_b)
+        if label_a is None or label_b is None:
+            continue
+        dist_a = cluster_distribution(assignments, label_a)
+        dist_b = cluster_distribution(assignments, label_b)
+        if not dist_a or not dist_b:
+            continue  # at least one member never appears in the corpus
+        top_a = max(dist_a, key=lambda c: dist_a[c])
+        top_b = max(dist_b, key=lambda c: dist_b[c])
+        shared = overlap(dist_a, dist_b)
+        rows.append(
+            {
+                "entity_id_a": id_a,
+                "entity_id_b": id_b,
+                "label_a": label_a,
+                "label_b": label_b,
+                "n_mentions_a": int(
+                    (assignments["entity"].astype(str) == label_a).sum()
+                ),
+                "n_mentions_b": int(
+                    (assignments["entity"].astype(str) == label_b).sum()
+                ),
+                "top_cluster_a": top_a,
+                "top_cluster_b": top_b,
+                "same_top_cluster": bool(top_a == top_b),
+                "mass_overlap": shared,
+                "disjoint": shared == 0.0,
+                "collides": bool(top_a == top_b or shared >= collision_threshold),
+            }
+        )
+
+    n = len(rows)
+    summary = {
+        "n_pairs_in_kb": len(pairs),
+        "n_pairs_observed": n,
+        "collision_threshold": collision_threshold,
+        "n_same_top_cluster": sum(r["same_top_cluster"] for r in rows),
+        "n_colliding": sum(r["collides"] for r in rows),
+        "n_disjoint": sum(r["disjoint"] for r in rows),
+        "collision_rate": (sum(r["collides"] for r in rows) / n) if n else None,
+        "disjoint_rate": (sum(r["disjoint"] for r in rows) / n) if n else None,
+        "mean_mass_overlap": (sum(r["mass_overlap"] for r in rows) / n) if n else None,
+    }
+    return rows, summary
+
+
+def _assignments_from_linker(model_path: str) -> pd.DataFrame:
+    from pelinker.model import Linker
+
+    linker = Linker.load(model_path)
+    frame = linker.training_cluster_frame
+    if frame is None:
+        raise click.ClickException(
+            "artifact carries no training_cluster_frame; pass --assignments-parquet"
+        )
+    return frame[["entity", "cluster"]].copy()
+
+
+def _assignments_from_fit_report(path: str) -> pd.DataFrame:
+    from pelinker.reports.io import read_clustering_report_json
+
+    report = read_clustering_report_json(path)
+    assignments = report.assignments
+    if assignments is None or assignments.empty:
+        raise click.ClickException(f"{path}: no assignments in the fit report")
+    return assignments[["entity", "cluster"]].copy()
+
+
+@click.command()
+@click.option("--kb-csv-path", required=True, type=ExpandedPath(exists=True))
+@click.option("--report-dir", required=True, type=ExpandedPath())
+@click.option(
+    "--model-path", default=None, type=ExpandedPath(), help="Fitted linker artifact."
+)
+@click.option(
+    "--fit-report",
+    default=None,
+    type=ExpandedPath(exists=True),
+    help="linker_fit.clustering_report.json.gz from a completed fit.",
+)
+@click.option(
+    "--assignments-parquet",
+    default=None,
+    type=ExpandedPath(exists=True),
+    help="Mention frame with 'entity' and 'cluster' columns.",
+)
+@click.option(
+    "--collision-threshold",
+    default=0.5,
+    show_default=True,
+    help="Shared cluster mass at or above which a pair counts as colliding.",
+)
+def main(
+    kb_csv_path: str,
+    report_dir: str,
+    model_path: str | None,
+    fit_report: str | None,
+    assignments_parquet: str | None,
+    collision_threshold: float,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    sources = [model_path, fit_report, assignments_parquet]
+    if sum(s is not None for s in sources) != 1:
+        raise click.ClickException(
+            "pass exactly one of --model-path / --fit-report / --assignments-parquet"
+        )
+
+    kb = pd.read_csv(kb_csv_path)
+    labels = {
+        str(r["entity_id"]): str(r["label"])
+        for _, r in kb.iterrows()
+        if not pd.isna(r["label"])
+    }
+    pairs = load_inverse_pairs(kb)
+    logger.info("KB inverse pairs with both members labelled: %d", len(pairs))
+
+    if model_path is not None:
+        assignments = _assignments_from_linker(model_path)
+    elif fit_report is not None:
+        assignments = _assignments_from_fit_report(fit_report)
+    else:
+        assignments = pd.read_parquet(
+            assignments_parquet, columns=["entity", "cluster"]
+        )
+    assignments = filter_emergent_assignments(assignments)
+    logger.info("Emergent mention rows: %d", len(assignments))
+
+    rows, summary = diagnose(
+        assignments, pairs, labels, collision_threshold=collision_threshold
+    )
+    logger.info("Summary: %s", json.dumps(summary, indent=1))
+
+    out = Path(report_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    pd.DataFrame(rows).sort_values("mass_overlap", ascending=False).to_csv(
+        out / "direction_pairs.csv", index=False
+    )
+    (out / "direction_summary.json").write_text(json.dumps(summary, indent=1))
+    logger.info("Wrote %d pair rows to %s", len(rows), out)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/analysis/model_selection.py b/run/analysis/model_selection.py
deleted file mode 100644
index 2e9f1a9..0000000
--- a/run/analysis/model_selection.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Thin shim — implementation lives in ``pelinker.cli.model_selection``."""
-
-from pelinker.cli.model_selection import main
-
-if __name__ == "__main__":
-    main()
diff --git a/run/analysis/oov_analysis.py b/run/analysis/oov_analysis.py
index 612d827..35dcfa9 100644
--- a/run/analysis/oov_analysis.py
+++ b/run/analysis/oov_analysis.py
@@ -53,7 +53,7 @@
     roc_curve,
 )
 
-from pelinker.io.json_files import load_json_path
+from pelinker.data.json_files import load_json_path
 
 # ─── Aesthetic constants ──────────────────────────────────────────────────────
 
diff --git a/run/analysis/replot.py b/run/analysis/replot.py
deleted file mode 100644
index 230b02c..0000000
--- a/run/analysis/replot.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Thin shim — implementation lives in ``pelinker.cli.replot``."""
-
-from pelinker.cli.replot import main
-
-if __name__ == "__main__":
-    main()
diff --git a/run/analysis/replot_fit.py b/run/analysis/replot_fit.py
index 3e5aed2..4f96679 100644
--- a/run/analysis/replot_fit.py
+++ b/run/analysis/replot_fit.py
@@ -26,7 +26,7 @@
 import pandas as pd
 import seaborn as sns
 
-from pelinker.cluster_composition_viz import (
+from pelinker.clustering.composition import (
     DEFAULT_MAX_CLUSTERS_FOR_PLOTS,
     DEFAULT_MAX_ENTITIES_FOR_FLOW_PLOTS,
     HDBSCAN_NOISE_CLUSTER_ID,
@@ -37,21 +37,24 @@
     with_noise_cluster_label,
 )
 
-from pelinker.kb_out import cluster_labels_from_catalog
+from pelinker.kb.kb_out import cluster_labels_from_catalog
 from pelinker.plotting import (
     build_fit_cluster_viz_plot_df,
     enrich_fit_cluster_viz_plot_df_with_context,
     plot_cluster_entity_sankey,
     plot_cluster_viz,
 )
-from pelinker.reporting import (
-    linker_fit_cluster_composition_path,
-    linker_fit_clustering_report_path,
-    linker_fit_kb_out_path,
+from pelinker.reports.io import (
     read_cluster_composition_json,
     read_clustering_report_json,
     read_kb_out_json,
 )
+from pelinker.reports.paths import (
+    linker_fit_cluster_composition_path,
+    linker_fit_clustering_report_path,
+    linker_fit_kb_out_path,
+)
+from pelinker.core.paths import ExpandedPath
 
 _PIE_SAMPLE_MAX_CLUSTERS = 6
 _FIGURE_EXTS = ("png", "pdf")
@@ -255,7 +258,7 @@ def _load_composition_df(
         if stored_exclude is True:
             needs_rebuild = True
         if needs_rebuild:
-            from pelinker.reporting import ModelSelectionReport
+            from pelinker.reports.schema import ModelSelectionReport
 
             if isinstance(report, ModelSelectionReport):
                 return build_cluster_composition_df(
@@ -266,7 +269,7 @@ def _load_composition_df(
                     max_clusters=max_clusters,
                 )
         return df
-    from pelinker.reporting import ModelSelectionReport
+    from pelinker.reports.schema import ModelSelectionReport
 
     if not isinstance(report, ModelSelectionReport):
         raise TypeError("report must be a ModelSelectionReport")
@@ -288,7 +291,7 @@ def _load_composition_df(
 )
 @click.argument(
     "report_dir",
-    type=click.Path(
+    type=ExpandedPath(
         path_type=pathlib.Path, exists=True, file_okay=False, dir_okay=True
     ),
 )
@@ -321,7 +324,7 @@ def _load_composition_df(
 )
 @click.option(
     "--pmid-text-table",
-    type=click.Path(path_type=pathlib.Path, dir_okay=False),
+    type=ExpandedPath(path_type=pathlib.Path, dir_okay=False),
     default=None,
     help=(
         "TSV/CSV (optional gzip) with PMID and full text columns. "
diff --git a/run/analysis/select_diverse_entities.py b/run/analysis/select_diverse_entities.py
index b005107..f43fbed 100644
--- a/run/analysis/select_diverse_entities.py
+++ b/run/analysis/select_diverse_entities.py
@@ -9,15 +9,17 @@
 from sklearn.decomposition import PCA
 
 from pelinker.model import Linker
-from pelinker.ops import load_dataframe
-from pelinker.util import load_models, embed_texts
+from pelinker.data.tables import load_dataframe
+from pelinker.text.embed import embed_texts
+from pelinker.text.models import load_models
 from sklearn.cluster import KMeans
 
-from pelinker.analysis import (
-    embeddings_dict_to_dataframe,
-    get_word_frequencies_from_library,
+from pelinker.data.frames import embeddings_dict_to_dataframe
+from pelinker.text.lexical import (
     compute_kb_generality_scores,
+    get_word_frequencies_from_library,
 )
+from pelinker.core.paths import ExpandedPath
 
 logger = logging.getLogger(__name__)
 
@@ -106,7 +108,7 @@ def select_diverse_entities(
 @click.command()
 @click.option(
     "--input-table-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help="Path to the dataframe to load (CSV/TSV, optionally gzipped).",
 )
@@ -134,7 +136,7 @@ def select_diverse_entities(
     "--model-type",
     type=click.STRING,
     default="pubmedbert",
-    help="Backbone model identifier passed to pelinker.util.load_models.",
+    help="Backbone model identifier passed to pelinker.text.models.load_models.",
 )
 @click.option(
     "--layers-spec",
@@ -167,7 +169,7 @@ def select_diverse_entities(
 )
 @click.option(
     "--output-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     required=True,
     help="Path for saving selected entities CSV file.",
 )
@@ -222,7 +224,7 @@ def run(
 
     # Load spacy model for texts_to_vrep
     logger.info("Loading spaCy model")
-    nlp = spacy.load("en_core_web_trf")
+    nlp = spacy.load("en_core_web_lg")
 
     # Filter rows where both id and label are not null
     df_filtered = df[[id_column, label_column]].dropna()
diff --git a/run/analysis/weak_label_check.py b/run/analysis/weak_label_check.py
new file mode 100644
index 0000000..124434b
--- /dev/null
+++ b/run/analysis/weak_label_check.py
@@ -0,0 +1,178 @@
+"""Acceptance check for the weak labels in a stage-(A) mention parquet.
+
+Run it on a small embedding run before launching a full grid. The weak labels are the
+target the hyperparameter searches optimise, so a defect here — one span carrying a
+relation *and* its converse, a symmetric relation given an inverse direction —
+silently becomes the objective.
+
+Checks (positive rows only; synthetic negatives are skipped):
+
+- **one label per span**: no ``(pmid, a_abs, b_abs)`` carries two labels;
+- **no nested sites**: no labelled span lies inside another labelled span of the same
+  document;
+- **no relation/converse collision**: no span is labelled with two members of one
+  converse pair (same ``canonical_entity_id`` in the pairs KB);
+- **symmetric stays symmetric**: no row whose label is symmetric has
+  ``direction=inverse``;
+- **every label is in the KB**: a label the KB does not hold cannot be given a class,
+  so the class views (:mod:`pelinker.kb.classes`) refuse the parquet.
+
+It also prints how labels were assigned (``surface_rule``, ``direction``), the most
+frequent labels, and — for a pairs KB — how many classes each view yields and how the
+mention mass splits by direction relative to the canonical relation. The ``reldir``
+count is the number of classes the selection objective scores against. Any violation
+makes the exit status non-zero.
+
+Usage:
+
+    uv run python run/analysis/weak_label_check.py \\
+        --parquet <dir>/res_pubmedbert_1.parquet \\
+        --kb-csv-path data/derived/properties.synthesis.2.pairs.csv
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import sys
+from typing import Any
+
+import click
+import pandas as pd
+
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.core.paths import ExpandedPath
+from pelinker.kb.classes import (
+    CANONICAL_COLUMNS,
+    CLASS_VIEWS,
+    ENTITY_CLASS_COLUMN,
+    RELATION_DIRECTION_COLUMN,
+    KbClasses,
+    add_view_columns,
+)
+
+logger = logging.getLogger(__name__)
+
+_COLUMNS = ["pmid", "entity", "a_abs", "b_abs", "direction", "surface_rule"]
+
+
+def load_mentions(path: str) -> pd.DataFrame:
+    """Positive mention rows, without the embedding column."""
+    frame = pd.read_parquet(path)
+    missing = [c for c in _COLUMNS if c not in frame.columns]
+    if missing:
+        raise click.ClickException(
+            f"{path}: no {', '.join(missing)} column(s) — the parquet predates "
+            "parse-based weak labels; re-embed it"
+        )
+    frame = frame[_COLUMNS]
+    return frame.loc[frame["entity"] != NEGATIVE_LABEL].reset_index(drop=True)
+
+
+def multi_label_spans(rows: pd.DataFrame) -> pd.DataFrame:
+    """Spans carrying more than one distinct label."""
+    per_span = rows.groupby(["pmid", "a_abs", "b_abs"])["entity"].nunique()
+    return per_span[per_span > 1].reset_index()
+
+
+def nested_spans(rows: pd.DataFrame) -> int:
+    """Labelled spans lying strictly inside another labelled span of the same document."""
+    n = 0
+    for _, doc in rows.drop_duplicates(["pmid", "a_abs", "b_abs"]).groupby("pmid"):
+        spans = list(zip(doc["a_abs"], doc["b_abs"]))
+        for a, b in spans:
+            if any((a2, b2) != (a, b) and a2 <= a and b <= b2 for a2, b2 in spans):
+                n += 1
+    return n
+
+
+def converse_collisions(rows: pd.DataFrame, kb: pd.DataFrame) -> int:
+    """Spans labelled with two different members of one converse pair."""
+    if "canonical_entity_id" not in kb.columns:
+        return 0
+    canon = dict(zip(kb["label"].astype(str), kb["canonical_entity_id"].astype(str)))
+    work = rows.assign(canonical=rows["entity"].map(canon))
+    grouped = work.groupby(["pmid", "a_abs", "b_abs", "canonical"])["entity"].nunique()
+    return int((grouped > 1).sum())
+
+
+def flipped_symmetric(rows: pd.DataFrame, kb: pd.DataFrame) -> int:
+    if "is_symmetric" not in kb.columns:
+        return 0
+    symmetric = set(kb.loc[kb["is_symmetric"].fillna(False).astype(bool), "label"])
+    return int(
+        (rows["entity"].isin(symmetric) & (rows["direction"] == "inverse")).sum()
+    )
+
+
+def labels_not_in_kb(rows: pd.DataFrame, kb: pd.DataFrame) -> list[str]:
+    """Mention labels the KB does not hold, sorted."""
+    return sorted(set(rows["entity"].astype(str)) - set(kb["label"].astype(str)))
+
+
+def view_summary(rows: pd.DataFrame, kb: pd.DataFrame) -> dict[str, Any] | None:
+    """Classes per view, and mass by direction relative to the canonical relation.
+
+    ``None`` for a KB without the converse-pair derivation: views need the pairs KB.
+    Labels outside the KB are left out here; :func:`labels_not_in_kb` reports them.
+    """
+    if "entity_id" not in kb.columns or any(
+        c not in kb.columns for c in CANONICAL_COLUMNS
+    ):
+        return None
+    classes = KbClasses.from_kb(kb)
+    known = rows.loc[rows["entity"].astype(str).isin(set(classes.canonical_label))]
+    summary: dict[str, Any] = {
+        f"classes_{view}": int(
+            add_view_columns(known, classes, view)[ENTITY_CLASS_COLUMN].nunique()
+        )
+        for view in CLASS_VIEWS
+    }
+    reldir = add_view_columns(known, classes, "reldir")
+    summary["relation_direction_mass"] = (
+        reldir[RELATION_DIRECTION_COLUMN].value_counts().to_dict()
+    )
+    return summary
+
+
+def check(rows: pd.DataFrame, kb: pd.DataFrame, top: int = 15) -> dict[str, Any]:
+    multi = multi_label_spans(rows)
+    unknown = labels_not_in_kb(rows, kb)
+    return {
+        "positive_rows": int(len(rows)),
+        "by_surface_rule": rows["surface_rule"].fillna("none").value_counts().to_dict(),
+        "by_direction": rows["direction"].fillna("none").value_counts().to_dict(),
+        "top_labels": rows["entity"].value_counts().head(top).to_dict(),
+        "views": view_summary(rows, kb),
+        "labels_not_in_kb": unknown[:top],
+        "violations": {
+            "multi_label_spans": int(len(multi)),
+            "nested_spans": nested_spans(rows),
+            "converse_collisions": converse_collisions(rows, kb),
+            "symmetric_with_inverse_direction": flipped_symmetric(rows, kb),
+            "labels_not_in_kb": len(unknown),
+        },
+    }
+
+
+@click.command()
+@click.option("--parquet", required=True, type=ExpandedPath(exists=True))
+@click.option("--kb-csv-path", required=True, type=ExpandedPath(exists=True))
+@click.option("--output-path", default=None, type=ExpandedPath())
+def main(parquet: str, kb_csv_path: str, output_path: str | None) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    report = check(load_mentions(parquet), pd.read_csv(kb_csv_path))
+    text = json.dumps(report, indent=1)
+    if output_path:
+        with open(output_path, "w", encoding="utf-8") as fh:
+            fh.write(text)
+    click.echo(text)
+    bad = {k: v for k, v in report["violations"].items() if v}
+    if bad:
+        logger.error("Weak-label violations: %s", bad)
+        sys.exit(1)
+    logger.info("Weak labels pass: one label per span, no converse collisions")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/embed_kb_corpus.py b/run/embed_kb_corpus.py
index 0831046..7564274 100644
--- a/run/embed_kb_corpus.py
+++ b/run/embed_kb_corpus.py
@@ -2,9 +2,10 @@
 import pathlib
 import logging
 
-from pelinker.config import EmbeddingModelMetadata, EmbeddingTrainingConfig
-from pelinker.embedder import embed_kb_corpus
-from pelinker.onto import NEGATIVE_LABEL
+from pelinker.core.config import EmbeddingModelMetadata, EmbeddingTrainingConfig
+from pelinker.embed.corpus import embed_kb_corpus
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.core.paths import ExpandedPath
 
 logger = logging.getLogger(__name__)
 
@@ -24,19 +25,19 @@
 )
 @click.option(
     "--input-text-table-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     default=pathlib.Path("data/test/mag_sample.tsv.gz"),
     help="Input file (TSV/CSV, optionally gzipped) with pmid and text columns. Headers are auto-detected.",
 )
 @click.option(
     "--kb-csv-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     default=pathlib.Path("data/derived/properties.synthesis.2.csv"),
     help="Path to csv with `label`, `entity_id` columns.",
 )
 @click.option(
     "--output-parquet-path",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     help="output Parquet file to append to",
 )
 @click.option(
@@ -62,7 +63,7 @@
 @click.option(
     "--nlp-model",
     type=click.STRING,
-    default="en_core_web_trf",
+    default="en_core_web_lg",
     help="spaCy model to use for tokenization/lemmas.",
 )
 @click.option(
diff --git a/run/eval/annotate_llm.py b/run/eval/annotate_llm.py
new file mode 100644
index 0000000..5cf9cdf
--- /dev/null
+++ b/run/eval/annotate_llm.py
@@ -0,0 +1,670 @@
+"""LLM pre-annotation of predicate mentions, for human verification.
+
+Reads the sampler's ``sample_texts.jsonl`` (+ ``sample_manifest.csv`` for roles) and the
+property KB, prompts an LLM per abstract, and writes gold-candidate annotations in the
+``*.gt.json`` document-list shape that ``pelinker.kb.ground_truth.load_ground_truth_spans``
+reads.
+
+Two deliberate design points:
+
+- **The model answers with labels, never ids.** Opaque ids carry no meaning for a
+  language model; resolving them here makes an unmatched label a logged rejection
+  instead of a plausible-looking wrong id.
+- **Only canonical labels are offered**, with orientation carried in ``direction``, so
+  a passive mention has exactly one valid encoding.
+- **The model never emits character offsets.** LLMs are unreliable at counting
+  characters, so the contract is verbatim ``surface`` strings plus a 1-based
+  ``occurrence`` index; this driver resolves offsets deterministically and rejects
+  anything that does not resolve (rejections land in ``annotate_report.json``, they are
+  never silently dropped).
+- **Responses are disk-cached** keyed on provider, model and prompts, so re-runs and
+  post-hoc parsing fixes are free — parsing happens after the cache, not before.
+
+Requires the ``eval`` extra (``uv sync --extra dev --extra eval``) and a credential for
+the chosen provider: Gemini by default (``GEMINI_API_KEY`` / ``GOOGLE_API_KEY``). Run it
+twice with different ``--provider`` / ``--model`` / ``--annotator`` values, both over
+``primary,double``, and compare with ``gold_review_sheet.py``. The second annotator
+serves two purposes: κ, and recall — verified gold only ever contains spans one of the
+annotators proposed. A second *model family* serves both better than a second checkpoint
+of the same one.
+
+For a reasoning model, pin ``--reasoning-effort`` and raise ``--max-tokens``: the
+reasoning counts against the output limit. A document whose answer was cut off is
+reported as ``truncated`` and left out of the output. Nothing is cached for it, so
+re-running with a larger limit retries only those documents.
+
+Usage:
+
+    uv run python run/eval/annotate_llm.py \
+        --sample-dir <workdir>/gold \
+        --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+        --output-path <workdir>/gold/gold.llm-a.json \
+        --roles primary,double
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from collections.abc import Callable
+from dataclasses import dataclass
+from pathlib import Path
+
+import click
+import pandas as pd
+import spacy
+
+from pelinker.eval import kb as eval_kb
+from pelinker.eval.kb_prompt import render_kb_catalog
+from pelinker.eval.llm import (
+    DEFAULT_MODEL,
+    DEFAULT_PROVIDER,
+    PROVIDERS,
+    LLMIncompleteError,
+    complete,
+)
+from pelinker.kb.ground_truth import GT_DIRECTIONS
+from pelinker.core.paths import ExpandedPath
+from pelinker.core.onto import SimplifiedToken
+from pelinker.text.mentions import split_predicate_labels
+from pelinker.text.tokenize import text_to_tokens
+
+logger = logging.getLogger(__name__)
+
+_PROMPT_PATH = Path(__file__).parent / "prompts" / "annotate_predicates.md"
+_FENCE_RE = re.compile(r"^```(?:json)?\s*|\s*```$", re.MULTILINE)
+
+
+@dataclass(frozen=True)
+class PromptParts:
+    system: str
+    user_template: str
+
+
+def load_prompt_template(path: Path) -> PromptParts:
+    """Split the template file on its ``## System`` / ``## User`` headings."""
+    text = path.read_text(encoding="utf-8")
+    try:
+        _, after_system = text.split("## System", 1)
+        system, user = after_system.split("## User", 1)
+    except ValueError as err:
+        raise click.ClickException(
+            f"{path}: expected '## System' and '## User' sections"
+        ) from err
+    return PromptParts(system=system.strip(), user_template=user.strip())
+
+
+def canonical_kb(kb: pd.DataFrame, *, kb_csv_path: str | None = None) -> pd.DataFrame:
+    """Only the canonical member of each converse pair is offered to the annotator.
+
+    A KB without ``is_canonical`` predates the pair derivation, so both members of every
+    converse pair would be offered and a passive mention would have two equally valid
+    encodings — the ambiguity the canonical vocabulary exists to remove. That is a silent
+    change to what the gold means, so it is refused rather than tolerated.
+    """
+    try:
+        return eval_kb.canonical_kb(kb, kb_csv_path=kb_csv_path)
+    except ValueError as err:
+        raise click.ClickException(str(err)) from err
+
+
+def kb_table_text(kb: pd.DataFrame) -> str:
+    """Numbered relation catalog for the prompt (see :mod:`pelinker.eval.kb_prompt`).
+
+    No ids: they mean nothing to the model, and transcribing them is a copying task that
+    fails silently. The converse form is shown so the model can recognise a passive
+    mention as this relation running the other way rather than reaching for a different
+    label.
+    """
+    return render_kb_catalog(kb)
+
+
+def build_label_index(kb: pd.DataFrame) -> dict[str, tuple[str, bool]]:
+    """Normalized label -> (canonical entity id, needs_direction_flip).
+
+    Built from the **whole** KB, not just the canonical half that the prompt offers. A
+    model that answers with a converse label ("expressed in" for "expresses") has made a
+    recoverable presentation error, not a semantic one: the relation is right and the
+    orientation is the other way round. Dropping those answers would discard converse-form
+    mentions specifically — the exact population the directionality analysis needs — so
+    they are mapped onto the canonical id with the direction flipped, and flagged.
+    """
+    canonical_of = eval_kb.canonical_id_map(kb)
+    index: dict[str, tuple[str, bool]] = {}
+    for _, r in kb.iterrows():
+        label = str(r["label"]).strip().casefold()
+        entity_id = str(r["entity_id"])
+        canonical = canonical_of.get(entity_id, entity_id)
+        index[label] = (canonical, canonical != entity_id)
+    return index
+
+
+_OPPOSITE_DIRECTION = {"forward": "inverse", "inverse": "forward"}
+
+
+def symmetric_ids(kb: pd.DataFrame) -> frozenset[str]:
+    """Canonical ids whose direction is always ``symmetric``."""
+    return eval_kb.symmetric_ids(kb)
+
+
+def verb_label_set(labels: list[str], nlp) -> frozenset[str]:
+    """Casefolded labels that are verb predicates (same test as the fit's matcher)."""
+    specs, _ = split_predicate_labels(labels, nlp)
+    return frozenset(spec.label.strip().casefold() for spec in specs)
+
+
+def label_anchors(kb: pd.DataFrame, nlp) -> dict[str, list[frozenset[str]]]:
+    """Canonical id -> content-lemma sets of every KB label folding onto it.
+
+    A hit is *anchored* when its surface carries one of these sets: the text uses the
+    label's own wording ("increased" for ``increases``, "caused by" for ``causes``). A hit
+    whose surface carries none is a *paraphrase* ("leads to" for ``causes``) — a valid
+    mention that a lemma-anchored linker never saw in training, so it is scored as its own
+    slice.
+    """
+    canonical_of = eval_kb.canonical_id_map(kb)
+    anchors: dict[str, list[frozenset[str]]] = {}
+    for _, row in kb.iterrows():
+        tokens = [t for t in text_to_tokens(nlp, str(row["label"])) if t.pos != "PUNCT"]
+        content = frozenset(t.lemma.lower() for t in tokens if not t.is_stop)
+        if not content:
+            content = frozenset(t.lemma.lower() for t in tokens)
+        entity_id = str(row["entity_id"])
+        anchors.setdefault(canonical_of.get(entity_id, entity_id), []).append(content)
+    return anchors
+
+
+def anchor_check(
+    tokens: list[SimplifiedToken], anchors: dict[str, list[frozenset[str]]]
+) -> Callable[[int, int, str], bool]:
+    """``(a, b, canonical_id) -> bool``: does the span use one of the id's label wordings?"""
+
+    def anchored(a: int, b: int, entity_id: str) -> bool:
+        lemmas = {t.lemma.lower() for t in tokens if t.ix < b and t.ix_end > a}
+        return any(s and s <= lemmas for s in anchors.get(entity_id, []))
+
+    return anchored
+
+
+def verb_span_check(tokens: list[SimplifiedToken]) -> Callable[[int, int], bool]:
+    """``(a, b) -> bool``: does the character span overlap a verb token?"""
+    verbs = [(t.ix, t.ix_end) for t in tokens if t.pos == "VERB"]
+
+    def has_verb(a: int, b: int) -> bool:
+        return any(start < b and end > a for start, end in verbs)
+
+    return has_verb
+
+
+def flip_direction(direction: str | None) -> str | None:
+    """Swap forward/inverse; ``symmetric`` and ``na`` have no opposite."""
+    if direction is None:
+        return None
+    return _OPPOSITE_DIRECTION.get(direction, direction)
+
+
+def _is_word_char(ch: str) -> bool:
+    return ch.isalnum() or ch == "_"
+
+
+def iter_occurrences(text: str, surface: str) -> list[tuple[int, int]]:
+    """All word-boundary-respecting occurrences of ``surface`` in ``text``.
+
+    Plain substring search silently mislocates a short answer inside a longer word —
+    "reduce" inside "reduces", "related to" inside "correlated to" — producing a span that
+    looks valid and points at the wrong token. Requiring word boundaries turns that into a
+    visible miss instead.
+    """
+    if not surface:
+        return []
+    spans: list[tuple[int, int]] = []
+    start = text.find(surface)
+    while start >= 0:
+        end = start + len(surface)
+        left_ok = start == 0 or not (
+            _is_word_char(text[start - 1]) and _is_word_char(surface[0])
+        )
+        right_ok = end == len(text) or not (
+            _is_word_char(text[end]) and _is_word_char(surface[-1])
+        )
+        if left_ok and right_ok:
+            spans.append((start, end))
+        start = text.find(surface, start + 1)
+    return spans
+
+
+def find_occurrence(
+    text: str,
+    surface: str,
+    occurrence: int,
+    *,
+    claimed: set[tuple[int, int]] | None = None,
+) -> tuple[tuple[int, int] | None, bool]:
+    """Resolve the ``occurrence``-th (1-based) ``surface``; returns (span, used_fallback).
+
+    Models count occurrences unreliably — far more unreliably than they quote text — so a
+    requested index that does not exist falls back to the first unclaimed occurrence of
+    the same surface. The surface itself is still required verbatim: this recovers the
+    *position*, never the content, and the fallback is recorded on the hit.
+    """
+    if not surface or occurrence < 1:
+        return None, False
+    spans = iter_occurrences(text, surface)
+    if not spans:
+        return None, False
+    taken = claimed or set()
+    if occurrence <= len(spans):
+        candidate = spans[occurrence - 1]
+        if candidate not in taken:
+            return candidate, False
+    # Either the index does not exist, or it points at a span an earlier record already
+    # took — models re-emit the same mention with the same index. Fall back to the first
+    # unclaimed occurrence; when every occurrence is spoken for, the record is a
+    # duplicate and is rejected rather than annotated twice.
+    for span in spans:
+        if span not in taken:
+            return span, True
+    return None, False
+
+
+def nearest_occurrence(text: str, needle: str, anchor: int) -> tuple[int, int] | None:
+    """Span of the occurrence of ``needle`` closest to char position ``anchor``."""
+    if not needle:
+        return None
+    spans: list[tuple[int, int]] = []
+    start = text.find(needle)
+    while start >= 0:
+        spans.append((start, start + len(needle)))
+        start = text.find(needle, start + 1)
+    if not spans:
+        return None
+    return min(spans, key=lambda s: abs(s[0] - anchor))
+
+
+def parse_llm_response(raw: str) -> list[dict]:
+    """Parse the model's JSON array, tolerating stray code fences.
+
+    An object with exactly one key holding a list (``{"annotations": [...]}``) is
+    unwrapped. Providers whose JSON mode can only produce an object wrap the requested
+    array this way; the records inside are what was asked for.
+    """
+    cleaned = _FENCE_RE.sub("", raw.strip()).strip()
+    data = json.loads(cleaned)
+    if isinstance(data, dict) and len(data) == 1:
+        (only,) = data.values()
+        if isinstance(only, list):
+            data = only
+    if not isinstance(data, list):
+        raise ValueError("expected a JSON array")
+    return [d for d in data if isinstance(d, dict)]
+
+
+def resolve_annotations(
+    text: str,
+    items: list[dict],
+    *,
+    label_index: dict[str, tuple[str, bool]],
+    annotator: str,
+    symmetric: frozenset[str] = frozenset(),
+    verb_labels: frozenset[str] = frozenset(),
+    has_verb: Callable[[int, int], bool] | None = None,
+    is_anchored: Callable[[int, int, str], bool] | None = None,
+) -> tuple[list[dict], list[dict]]:
+    """LLM records → offset-resolved gt hits; unresolvable records are reported.
+
+    Three recoveries are applied and flagged rather than dropped, because dropping any
+    would bias the gold set rather than merely shrink it: a mis-counted ``occurrence``
+    (which loses repeated mentions), a converse label used in place of its canonical
+    partner (which loses inverse-direction mentions specifically), and an oriented
+    ``direction`` on a relation in ``symmetric`` (the label is right; orientation does
+    not apply to it, so the direction is set to ``symmetric``).
+
+    One rejection is specific to scope: when ``has_verb`` is given, a record whose label
+    is in ``verb_labels`` (casefolded) but whose span contains no verb — a nominal form
+    such as "association" or "inhibition" — is rejected as ``non_verbal``. The linker is
+    trained on verb mentions only, so a noun in gold would score a skill it is never
+    taught. Labels that are not verb predicates ("has part") keep noun surfaces.
+    """
+    hits: list[dict] = []
+    rejected: list[dict] = []
+    claimed: set[tuple[int, int]] = set()
+    for item in items:
+        surface = str(item.get("surface") or "")
+        occurrence = int(item.get("occurrence") or 1)
+        label = str(item.get("label") or "")
+        resolved = label_index.get(label.strip().casefold())
+        direction = item.get("direction")
+        reasons = []
+        span, occurrence_fallback = find_occurrence(
+            text, surface, occurrence, claimed=claimed
+        )
+        if span is None:
+            # Present but every occurrence already taken: the model re-emitted a
+            # mention, which is a different failure from quoting text that is not there.
+            reasons.append(
+                "duplicate_occurrence"
+                if iter_occurrences(text, surface)
+                else "surface_not_found"
+            )
+        if resolved is None:
+            reasons.append("unknown_label")
+        if direction is not None and direction not in GT_DIRECTIONS:
+            reasons.append("bad_direction")
+        if (
+            span is not None
+            and has_verb is not None
+            and label.strip().casefold() in verb_labels
+            and not has_verb(*span)
+        ):
+            reasons.append("non_verbal")
+        if reasons:
+            rejected.append({**item, "reasons": reasons})
+            continue
+        assert span is not None and resolved is not None
+        entity_id, needs_flip = resolved
+        if needs_flip:
+            direction = flip_direction(direction)
+        direction_coerced = entity_id in symmetric and direction != "symmetric"
+        if direction_coerced:
+            direction = "symmetric"
+        a, b = span
+        claimed.add(span)
+        hit: dict = {
+            "a": a,
+            "b": b,
+            "entity_id": entity_id,
+            "label": label,
+            "surface": surface,
+            "direction": direction,
+            "annotator": annotator,
+            "source": "llm",
+        }
+        if occurrence_fallback:
+            hit["occurrence_fallback"] = True
+        if needs_flip:
+            hit["label_canonicalized"] = True
+        if direction_coerced:
+            hit["direction_coerced"] = True
+        if is_anchored is not None:
+            hit["surface_anchored"] = is_anchored(a, b, entity_id)
+        if item.get("confidence") is not None:
+            hit["confidence"] = float(item["confidence"])
+        for key, field in (
+            ("subject_text", "subject_span"),
+            ("object_text", "object_span"),
+        ):
+            arg = item.get(key)
+            if arg:
+                arg_span = nearest_occurrence(text, str(arg), a)
+                if arg_span is not None:
+                    hit[field] = list(arg_span)
+        hits.append(hit)
+
+    hits, nested = drop_nested_spans(hits)
+    rejected.extend(nested)
+    return hits, rejected
+
+
+def drop_nested_spans(hits: list[dict]) -> tuple[list[dict], list[dict]]:
+    """Keep one annotation per mention site: the longest span wins.
+
+    A model routinely emits both the bare verb and the verb-plus-preposition for one site
+    ("expressed" and "expressed in"). Keeping both would put two labels on one mention —
+    the very defect this gold set exists to measure in the pipeline's own supervision.
+    """
+    kept: list[dict] = []
+    dropped: list[dict] = []
+    ordered = sorted(hits, key=lambda h: h["b"] - h["a"], reverse=True)
+    for hit in ordered:
+        if any(k["a"] <= hit["a"] and hit["b"] <= k["b"] for k in kept):
+            dropped.append({**hit, "reasons": ["nested_in_longer_span"]})
+        else:
+            kept.append(hit)
+    kept.sort(key=lambda h: (h["a"], h["b"]))
+    return kept, dropped
+
+
+@click.command()
+@click.option("--sample-dir", required=True, type=ExpandedPath(exists=True))
+@click.option("--kb-csv-path", required=True, type=ExpandedPath(exists=True))
+@click.option("--output-path", required=True, type=ExpandedPath())
+@click.option(
+    "--provider",
+    type=click.Choice(PROVIDERS),
+    default=DEFAULT_PROVIDER,
+    show_default=True,
+)
+@click.option("--model", default=DEFAULT_MODEL, show_default=True)
+@click.option(
+    "--reasoning-effort",
+    default=None,
+    help=(
+        "Reasoning effort for a reasoning model (openai only), e.g. low / medium. "
+        "Part of the cache key, so each setting is its own annotation run."
+    ),
+)
+@click.option(
+    "--annotator",
+    default=None,
+    help=(
+        "Annotator tag recorded on every hit "
+        "(default: the model id, plus @<effort> when --reasoning-effort is set)."
+    ),
+)
+@click.option(
+    "--roles",
+    default="primary,double",
+    show_default=True,
+    help="Comma-separated manifest roles to annotate.",
+)
+@click.option(
+    "--cache-dir",
+    default=None,
+    type=ExpandedPath(file_okay=False),
+    help="LLM response cache (default: <sample-dir>/.llm_cache).",
+)
+@click.option("--prompt-path", default=str(_PROMPT_PATH), show_default=True)
+@click.option("--max-tokens", default=8000, show_default=True)
+@click.option("--limit", default=None, type=int, help="Annotate only the first N docs.")
+@click.option(
+    "--dry-run",
+    is_flag=True,
+    help="Print request sizes and exit without calling the LLM.",
+)
+@click.option(
+    "--verbal-only/--no-verbal-only",
+    default=True,
+    show_default=True,
+    help=(
+        "Reject hits on verb-predicate labels whose span has no verb (nominal forms). "
+        "Applied after the response cache, so toggling it costs no LLM calls."
+    ),
+)
+@click.option("--nlp-model", default="en_core_web_lg", show_default=True)
+def main(
+    sample_dir: str,
+    kb_csv_path: str,
+    output_path: str,
+    provider: str,
+    model: str,
+    reasoning_effort: str | None,
+    annotator: str | None,
+    roles: str,
+    cache_dir: str | None,
+    prompt_path: str,
+    max_tokens: int,
+    limit: int | None,
+    dry_run: bool,
+    verbal_only: bool,
+    nlp_model: str,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    annotator = annotator or (
+        f"{model}@{reasoning_effort}" if reasoning_effort else model
+    )
+    sample = Path(sample_dir)
+    cache = Path(cache_dir) if cache_dir else sample / ".llm_cache"
+
+    manifest = pd.read_csv(sample / "sample_manifest.csv")
+    wanted_roles = {r.strip() for r in roles.split(",") if r.strip()}
+    wanted_ids = set(
+        manifest.loc[manifest["role"].isin(wanted_roles), "doc_id"].astype(int)
+    )
+
+    docs: list[dict] = []
+    with (sample / "sample_texts.jsonl").open(encoding="utf-8") as fh:
+        for line in fh:
+            rec = json.loads(line)
+            if int(rec["doc_id"]) in wanted_ids:
+                docs.append(rec)
+    if limit is not None:
+        docs = docs[:limit]
+    logger.info("Annotating %d docs (roles: %s)", len(docs), sorted(wanted_roles))
+
+    kb_all = pd.read_csv(kb_csv_path)
+    kb = canonical_kb(kb_all, kb_csv_path=kb_csv_path)
+    pending = eval_kb.pending_review_pairs(kb_all)
+    if len(pending):
+        # A heuristic pair that turns out to be wrong merges two distinct relations onto
+        # one canonical id, and every downstream artifact then agrees with the mistake.
+        logger.warning(
+            "%d KB rows are in converse pairs no curator has reviewed "
+            "(needs_review=True); annotating on top of them bakes any wrong pair into "
+            "the gold. Review them via --review-csv in %s.",
+            len(pending),
+            "run/preprocessing/derive_inverse_pairs.py",
+        )
+    # Prompt: canonical only. Decoder: the whole KB, so a converse answer is recovered
+    # (with its direction flipped) instead of discarded.
+    label_index = build_label_index(kb_all)
+    symmetric = symmetric_ids(kb_all)
+    nlp = None if dry_run else spacy.load(nlp_model)
+    verb_labels = (
+        verb_label_set(kb_all["label"].dropna().astype(str).tolist(), nlp)
+        if nlp is not None and verbal_only
+        else frozenset()
+    )
+    anchors = label_anchors(kb_all, nlp) if nlp is not None else {}
+    logger.info(
+        "Offering %d canonical relation labels (decoder accepts %d incl. converse "
+        "forms; %d symmetric)",
+        len(kb),
+        len(label_index),
+        len(symmetric),
+    )
+    prompt = load_prompt_template(Path(prompt_path))
+    system = prompt.system.replace("{kb_table}", kb_table_text(kb))
+
+    if dry_run:
+        user_chars = [len(prompt.user_template) + len(d["text"]) for d in docs]
+        logger.info(
+            "Dry run: system=%d chars (cached prefix), %d requests, "
+            "user chars total=%d mean=%.0f",
+            len(system),
+            len(docs),
+            sum(user_chars),
+            (sum(user_chars) / len(user_chars)) if user_chars else 0.0,
+        )
+        return
+
+    out_docs: list[dict] = []
+    all_rejected: list[dict] = []
+    n_hits = 0
+    n_truncated = 0
+    for itext, doc in enumerate(docs):
+        text = doc["text"]
+        user = prompt.user_template.replace("{abstract}", text)
+        try:
+            raw = complete(
+                system=system,
+                user=user,
+                cache_dir=cache,
+                provider=provider,
+                model=model,
+                max_output_tokens=max_tokens,
+                # The prompt asks for a JSON array; have the provider guarantee that
+                # shape rather than parsing it back out of prose.
+                json_mode=True,
+                reasoning_effort=reasoning_effort,
+            )
+        except LLMIncompleteError as err:
+            # Left out of the output rather than written with no hits: an empty
+            # document would read as "the annotator found nothing" in the agreement
+            # stats, when in fact it never answered.
+            logger.warning("doc_id=%s: %s", doc["doc_id"], err)
+            all_rejected.append(
+                {"doc_id": doc["doc_id"], "reasons": ["truncated"], "detail": str(err)}
+            )
+            n_truncated += 1
+            continue
+        try:
+            items = parse_llm_response(raw)
+        except (ValueError, json.JSONDecodeError):
+            logger.warning("doc_id=%s: unparsable response", doc["doc_id"])
+            all_rejected.append({"doc_id": doc["doc_id"], "reasons": ["unparsable"]})
+            items = []
+        assert nlp is not None  # dry runs return before decoding
+        doc_tokens = text_to_tokens(nlp, text)
+        hits, rejected = resolve_annotations(
+            text,
+            items,
+            label_index=label_index,
+            annotator=annotator,
+            symmetric=symmetric,
+            verb_labels=verb_labels,
+            has_verb=verb_span_check(doc_tokens) if verbal_only else None,
+            is_anchored=anchor_check(doc_tokens, anchors),
+        )
+        for r in rejected:
+            all_rejected.append({"doc_id": doc["doc_id"], **r})
+        for h in hits:
+            h["itext"] = itext
+        n_hits += len(hits)
+        out_docs.append(
+            {
+                "doc_id": doc["doc_id"],
+                # Provenance travels with the annotation: a gold span whose document
+                # cannot be cited is not usable evidence.
+                "doc_uid": doc.get("doc_uid"),
+                "ids": doc.get("ids", {}),
+                "text": text,
+                "ground_truth": hits,
+            }
+        )
+
+    out = Path(output_path)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(out_docs, ensure_ascii=False, indent=1), encoding="utf-8")
+    report = {
+        "provider": provider,
+        "model": model,
+        "reasoning_effort": reasoning_effort,
+        "annotator": annotator,
+        "n_docs": len(out_docs),
+        "n_truncated": n_truncated,
+        "n_hits": n_hits,
+        "n_rejected": len(all_rejected),
+        "rejected": all_rejected,
+    }
+    report_path = out.with_suffix(".report.json")
+    report_path.write_text(json.dumps(report, indent=1), encoding="utf-8")
+    logger.info(
+        "Wrote %d docs / %d hits to %s (%d rejected records → %s)",
+        len(out_docs),
+        n_hits,
+        out,
+        len(all_rejected),
+        report_path,
+    )
+    if n_truncated:
+        logger.warning(
+            "%d docs were cut off at --max-tokens=%d and left out; re-run with a larger "
+            "limit to retry only those (nothing was cached for them)",
+            n_truncated,
+            max_tokens,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/eval/audit_sample.py b/run/eval/audit_sample.py
new file mode 100644
index 0000000..9b97718
--- /dev/null
+++ b/run/eval/audit_sample.py
@@ -0,0 +1,285 @@
+"""Audit a gold sample and report KB coverage — run after every batch.
+
+Two questions, answered from the artifacts in a gold directory:
+
+**Is the sample sound?**
+
+- **Eligibility.** Re-applies the gate (:mod:`pelinker.eval.sample_quality`) to every
+  drawn text and counts failures by reason. A sample drawn by the current sampler shows
+  zero failures; an older draw shows what it let through.
+- **Density vs length.** The correlation between the stratification density and abstract
+  length. If it is high, the strata are length strata in disguise.
+- **Empty documents.** Documents with no gold hit, by stratum. Negative controls — strata
+  ending in ``-none`` (or ``-zero`` in older samples) — *should* be empty; any hit there is
+  a mention the detector missed.
+- **Concentration.** How much of the gold mass sits on the ten most frequent labels, and
+  how many labels occur only once.
+- **Paraphrases and corrections.** The share of hits whose surface does not use the
+  label's wording (``surface_anchored`` false), and — for a verified gold file — the
+  curator's accept / fix / reject counts from ``<gold file>.verification.json``.
+
+**How far has coverage come?**
+
+Per canonical label: abstracts in the eligible pool where the detector finds it, gold hits
+so far, and whether it has reached ``--min-mentions`` (the smallest class a partition
+metric can use). ``reachable`` counts the labels the pool supports at that level. The
+cumulative curve over batches is the stopping rule: grow the sample until newly covered
+labels per batch level off.
+
+Usage:
+
+    uv run python run/eval/audit_sample.py \\
+        --gold-dir <workdir>/gold \\
+        --kb-csv-path data/derived/properties.synthesis.2.pairs.csv
+
+Writes ``<gold-dir>/sample_audit.json`` (override with ``--output-path``). Numbers are
+measurement output; cite them from the measurement writeup, not from this repository.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+import click
+import pandas as pd
+
+from pelinker.core.paths import ExpandedPath
+from pelinker.eval import sample_quality as sq
+
+logger = logging.getLogger(__name__)
+
+NEGATIVE_SUFFIXES = ("-none", "-zero")
+
+
+def load_gold_hits(path: Path) -> dict[int, list[dict]]:
+    """``doc_id -> ground_truth`` from a ``*.gt.json``-shaped gold file."""
+    return {
+        int(d["doc_id"]): list(d.get("ground_truth", []))
+        for d in json.loads(path.read_text(encoding="utf-8"))
+    }
+
+
+def canonical_label_map(kb: pd.DataFrame) -> tuple[dict[str, str], dict[str, str]]:
+    """``(entity_id -> canonical label, label -> canonical label)``."""
+    label_of = dict(zip(kb["entity_id"].astype(str), kb["label"].astype(str)))
+    canon = (
+        kb["canonical_entity_id"].astype(str)
+        if "canonical_entity_id" in kb.columns
+        else kb["entity_id"].astype(str)
+    )
+    by_id = {
+        e: label_of.get(c, label_of[e])
+        for e, c in zip(kb["entity_id"].astype(str), canon)
+    }
+    by_label = {label_of[e]: by_id[e] for e in by_id}
+    return by_id, by_label
+
+
+def density_column(manifest: pd.DataFrame) -> str | None:
+    for col in ("n_verb_mentions", "n_mentions_proxy"):
+        if col in manifest.columns:
+            return col
+    return None
+
+
+def quality_section(manifest: pd.DataFrame, texts: dict[int, str]) -> dict[str, Any]:
+    rules = sq.Eligibility()
+    reasons = [
+        sq.drop_reason(texts.get(int(d), ""), y, rules)
+        for d, y in zip(manifest["doc_id"], manifest["publication_year"])
+    ]
+    lengths = manifest["doc_id"].map(lambda d: len(texts.get(int(d), "")))
+    out: dict[str, Any] = {"eligibility": sq.reason_counts(reasons)}
+    col = density_column(manifest)
+    if col is not None and len(manifest) > 2:
+        out["density_column"] = col
+        out["corr_density_length"] = round(float(manifest[col].corr(lengths)), 3)
+    return out
+
+
+def gold_section(
+    manifest: pd.DataFrame,
+    gold: dict[int, list[dict]],
+    by_id: dict[str, str],
+    min_mentions: int,
+) -> tuple[dict[str, Any], pd.Series]:
+    n_hits = manifest["doc_id"].map(lambda d: len(gold.get(int(d), [])))
+    stratum = manifest["stratum"].astype(str)
+    negative = stratum.str.endswith(NEGATIVE_SUFFIXES)
+    labels = pd.Series(
+        [
+            by_id.get(str(h.get("entity_id")), str(h.get("entity_id")))
+            for d in manifest["doc_id"]
+            for h in gold.get(int(d), [])
+        ],
+        dtype=object,
+    )
+    counts = labels.value_counts()
+    section = {
+        "documents": int(len(manifest)),
+        "hits": int(n_hits.sum()),
+        "documents_without_hits": int((n_hits == 0).sum()),
+        "documents_without_hits_excluding_negatives": int(
+            ((n_hits == 0) & ~negative).sum()
+        ),
+        "negative_controls": int(negative.sum()),
+        "negative_controls_with_hits": int(((n_hits > 0) & negative).sum()),
+        "without_hits_by_stratum": {
+            k: f"{int(v.sum())}/{len(v)}" for k, v in (n_hits == 0).groupby(stratum)
+        },
+        "distinct_labels": int(counts.size),
+        "labels_at_min_mentions": int((counts >= min_mentions).sum()),
+        "top10_share": round(float(counts.head(10).sum() / max(1, counts.sum())), 3),
+        "singleton_labels": int((counts == 1).sum()),
+    }
+    flags = [
+        h["surface_anchored"]
+        for d in manifest["doc_id"]
+        for h in gold.get(int(d), [])
+        if "surface_anchored" in h
+    ]
+    if flags:
+        # Paraphrases ("leads to" for `causes`) are valid gold; the linker's training is
+        # lemma-anchored, so they are scored as their own slice.
+        section["paraphrase_hits"] = int(sum(not f for f in flags))
+        section["paraphrase_share"] = round(sum(not f for f in flags) / len(flags), 3)
+    return section, counts
+
+
+def coverage_section(
+    manifest: pd.DataFrame,
+    gold: dict[int, list[dict]],
+    counts: pd.Series,
+    pool_labels: pd.Series | None,
+    by_id: dict[str, str],
+    by_label: dict[str, str],
+    min_mentions: int,
+) -> dict[str, Any]:
+    section: dict[str, Any] = {"min_mentions": min_mentions}
+    pool_freq: pd.Series | None = None
+    if pool_labels is not None:
+        canon = [
+            by_label.get(label, label)
+            for value in pool_labels
+            if isinstance(value, str) and value
+            for label in set(value.split("|"))
+        ]
+        pool_freq = pd.Series(canon, dtype=object).value_counts()
+        reachable = set(pool_freq[pool_freq >= min_mentions].index)
+        covered = {k for k, v in counts.items() if v >= min_mentions}
+        section["reachable_labels"] = len(reachable)
+        section["covered_reachable"] = len(reachable & covered)
+    if "batch" in manifest.columns:
+        curve = []
+        seen: dict[str, int] = {}
+        for batch, rows in manifest.sort_values("batch").groupby("batch"):
+            for d in rows["doc_id"]:
+                for h in gold.get(int(d), []):
+                    key = by_id.get(str(h.get("entity_id")), str(h.get("entity_id")))
+                    seen[key] = seen.get(key, 0) + 1
+            curve.append(
+                {
+                    "batch": int(str(batch)),
+                    "documents": int(len(rows)),
+                    "labels_at_min_mentions": sum(
+                        v >= min_mentions for v in seen.values()
+                    ),
+                    "distinct_labels": len(seen),
+                }
+            )
+        section["curve"] = curve
+    table = pd.DataFrame({"gold_hits": counts})
+    if pool_freq is not None:
+        table = table.join(pool_freq.rename("pool_abstracts"), how="outer")
+    table = (
+        table.fillna(0)
+        .astype(int)
+        .sort_values(
+            [c for c in ("pool_abstracts", "gold_hits") if c in table.columns],
+            ascending=False,
+        )
+    )
+    section["per_label"] = table.reset_index(names="label").to_dict(orient="records")
+    return section
+
+
+@click.command()
+@click.option("--gold-dir", required=True, type=ExpandedPath(exists=True))
+@click.option("--kb-csv-path", required=True, type=ExpandedPath(exists=True))
+@click.option(
+    "--gold-file",
+    default="gold.llm-a.json",
+    show_default=True,
+    help="Gold file inside --gold-dir; a verified file supersedes the LLM draft.",
+)
+@click.option("--min-mentions", default=5, show_default=True)
+@click.option("--output-path", default=None, type=ExpandedPath())
+def main(
+    gold_dir: str,
+    kb_csv_path: str,
+    gold_file: str,
+    min_mentions: int,
+    output_path: str | None,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    root = Path(gold_dir)
+    manifest = pd.read_csv(root / "sample_manifest.csv")
+    texts: dict[int, str] = {}
+    with (root / "sample_texts.jsonl").open(encoding="utf-8") as fh:
+        for line in fh:
+            rec = json.loads(line)
+            texts[int(rec["doc_id"])] = rec["text"]
+    kb = pd.read_csv(kb_csv_path)
+    by_id, by_label = canonical_label_map(kb)
+
+    audit: dict[str, Any] = {
+        "gold_dir": str(root),
+        "quality": quality_section(manifest, texts),
+    }
+    gold_path = root / gold_file
+    if gold_path.exists():
+        gold = load_gold_hits(gold_path)
+        audit["gold_file"] = gold_file
+        # Only documents the gold file covers: reserve documents are never annotated, and
+        # counting them as "without hits" would read as annotation failures.
+        annotated = manifest.loc[manifest["doc_id"].astype(int).isin(gold)]
+        audit["documents_not_in_gold_file"] = int(len(manifest) - len(annotated))
+        audit["gold"], counts = gold_section(annotated, gold, by_id, min_mentions)
+        verification = gold_path.with_suffix(".verification.json")
+        if verification.exists():
+            # Written by `gold_review_sheet.py import`: accept / fix / reject counts.
+            audit["verification"] = json.loads(verification.read_text(encoding="utf-8"))
+        pool_path = root / "pool_mentions.csv.gz"
+        pool_labels = (
+            pd.read_csv(pool_path)["labels_detected"] if pool_path.exists() else None
+        )
+        audit["coverage"] = coverage_section(
+            annotated, gold, counts, pool_labels, by_id, by_label, min_mentions
+        )
+    else:
+        logger.warning(
+            "No %s in %s — gold and coverage sections skipped", gold_file, root
+        )
+
+    out = Path(output_path) if output_path else root / "sample_audit.json"
+    out.write_text(json.dumps(audit, indent=1), encoding="utf-8")
+    summary = {
+        k: v for k, v in audit.get("gold", {}).items() if k != "without_hits_by_stratum"
+    }
+    logger.info("Quality: %s", audit["quality"])
+    logger.info("Gold: %s", summary)
+    if "coverage" in audit:
+        cov = {
+            k: v
+            for k, v in audit["coverage"].items()
+            if k not in ("per_label", "curve")
+        }
+        logger.info("Coverage: %s", cov)
+    logger.info("Wrote %s", out)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/eval/dataset_stats.py b/run/eval/dataset_stats.py
new file mode 100644
index 0000000..6fe0944
--- /dev/null
+++ b/run/eval/dataset_stats.py
@@ -0,0 +1,173 @@
+"""Corpus, KB and gold-set statistics for the evaluation tables.
+
+The submission reported no corpus size, no mention counts, and no dataset table at all —
+a reviewer could not tell how much data any number rested on. This produces those figures
+from the artifacts themselves rather than from memory.
+
+Any subset of the three inputs may be given:
+
+- ``--input-text-table-path`` — abstract table (``ids``/``publication_year``/``summary``):
+  document count, year range, character-length distribution, pmid coverage.
+- ``--kb-csv-path`` — property KB: entity count, labels with descriptions, label token
+  lengths, declared inverse pairs.
+- ``--fit-report`` — a fit's clustering report: mention rows, emergent clusters, noise
+  fraction, mentions per KB label, and the share of label mass that is passive-voice.
+- ``--gold`` — verified gold JSON: documents, spans, spans per document, direction
+  distribution, label coverage.
+
+Numbers are measurement output: write them outside this repository and cite them from the
+measurement writeup.
+
+Usage:
+
+    uv run python run/eval/dataset_stats.py \
+        --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+        --fit-report <report-dir>/linker_fit.clustering_report.json.gz \
+        --report-dir <workdir>/eval-runs/dataset-stats
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+import click
+import pandas as pd
+from pelinker.core.paths import ExpandedPath
+
+logger = logging.getLogger(__name__)
+
+
+def corpus_stats(path: str) -> dict[str, Any]:
+    frame = pd.read_csv(path, sep="\t")
+    text = frame["summary"].fillna("").astype(str)
+    lengths = text.str.len()
+    return {
+        "n_documents": int(len(frame)),
+        "n_with_pmid": int(
+            frame["ids"].astype(str).str.contains("pubmed.ncbi.nlm.nih.gov").sum()
+        )
+        if "ids" in frame.columns
+        else None,
+        "publication_year_min": int(frame["publication_year"].min())
+        if "publication_year" in frame.columns
+        else None,
+        "publication_year_max": int(frame["publication_year"].max())
+        if "publication_year" in frame.columns
+        else None,
+        "chars_mean": float(lengths.mean()),
+        "chars_median": float(lengths.median()),
+        "chars_p90": float(lengths.quantile(0.9)),
+        "n_empty": int((lengths == 0).sum()),
+    }
+
+
+def kb_stats(path: str) -> dict[str, Any]:
+    kb = pd.read_csv(path)
+    labelled = kb[kb["label"].notna()]
+    token_counts = labelled["label"].astype(str).str.split().map(len)
+    out: dict[str, Any] = {
+        "n_entities": int(len(kb)),
+        "n_labelled": int(len(labelled)),
+        "n_with_description": int(labelled["description"].notna().sum())
+        if "description" in labelled.columns
+        else None,
+        "label_tokens_mean": float(token_counts.mean()),
+        "label_tokens_max": int(token_counts.max()),
+        "n_multiword_labels": int((token_counts > 1).sum()),
+    }
+    if "inverse_entity_id" in kb.columns:
+        known = set(kb["entity_id"].astype(str))
+        resolvable = kb["inverse_entity_id"].dropna().astype(str).isin(known)
+        out["n_declared_inverses"] = int(kb["inverse_entity_id"].notna().sum())
+        out["n_resolvable_inverses"] = int(resolvable.sum())
+    return out
+
+
+def fit_stats(path: str) -> dict[str, Any]:
+    from pelinker.reports.io import read_clustering_report_json
+
+    report = read_clustering_report_json(path)
+    assignments = report.assignments
+    emergent = assignments[assignments["cluster"].astype(int) >= 0]
+    per_label = assignments["entity"].astype(str).value_counts()
+    passive = assignments["entity"].astype(str).str.endswith(" by")
+    return {
+        "n_mention_rows": int(len(assignments)),
+        "n_emergent_rows": int(len(emergent)),
+        "noise_fraction": float(1.0 - len(emergent) / len(assignments))
+        if len(assignments)
+        else None,
+        "n_emergent_clusters": int(emergent["cluster"].nunique()),
+        "n_labels_present": int(assignments["entity"].nunique()),
+        "mentions_per_label_median": float(per_label.median()),
+        "mentions_per_label_p90": float(per_label.quantile(0.9)),
+        "mentions_per_label_max": int(per_label.max()),
+        "passive_label_mention_share": float(passive.mean()),
+        "min_cluster_size": int(report.hyperparameters.min_cluster_size),
+    }
+
+
+def gold_stats(path: str) -> dict[str, Any]:
+    from pelinker.eval.harness import load_gold_docs
+
+    docs = load_gold_docs(path)
+    spans = [s for d in docs for s in d.spans]
+    per_doc = [len(d.spans) for d in docs]
+    directions = pd.Series(
+        [s.direction for s in spans if s.direction is not None], dtype=object
+    )
+    sources = pd.Series([s.source for s in spans if s.source is not None], dtype=object)
+    return {
+        "n_documents": len(docs),
+        "n_spans": len(spans),
+        "spans_per_doc_mean": float(pd.Series(per_doc).mean()) if per_doc else None,
+        "n_documents_without_spans": int(sum(1 for n in per_doc if n == 0)),
+        "n_distinct_labels": len({s.entity_id for s in spans if s.entity_id}),
+        "direction_counts": directions.value_counts().to_dict(),
+        "source_counts": sources.value_counts().to_dict(),
+    }
+
+
+@click.command()
+@click.option("--input-text-table-path", default=None, type=ExpandedPath(exists=True))
+@click.option("--kb-csv-path", default=None, type=ExpandedPath(exists=True))
+@click.option("--fit-report", default=None, type=ExpandedPath(exists=True))
+@click.option("--gold", default=None, type=ExpandedPath(exists=True))
+@click.option("--report-dir", required=True, type=ExpandedPath())
+def main(
+    input_text_table_path: str | None,
+    kb_csv_path: str | None,
+    fit_report: str | None,
+    gold: str | None,
+    report_dir: str,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    if not any([input_text_table_path, kb_csv_path, fit_report, gold]):
+        raise click.ClickException("give at least one input to describe")
+
+    stats: dict[str, Any] = {}
+    if input_text_table_path:
+        stats["corpus"] = corpus_stats(input_text_table_path)
+        stats["corpus"]["path"] = input_text_table_path
+    if kb_csv_path:
+        stats["kb"] = kb_stats(kb_csv_path)
+        stats["kb"]["path"] = kb_csv_path
+    if fit_report:
+        stats["fit"] = fit_stats(fit_report)
+        stats["fit"]["path"] = fit_report
+    if gold:
+        stats["gold"] = gold_stats(gold)
+        stats["gold"]["path"] = gold
+
+    logger.info("%s", json.dumps(stats, indent=1, default=str))
+    out = Path(report_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    (out / "dataset_stats.json").write_text(json.dumps(stats, indent=1, default=str))
+    logger.info("Wrote %s", out / "dataset_stats.json")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/eval/gold_review_sheet.py b/run/eval/gold_review_sheet.py
new file mode 100644
index 0000000..8738e38
--- /dev/null
+++ b/run/eval/gold_review_sheet.py
@@ -0,0 +1,500 @@
+"""Human-verification round-trip for LLM-pre-annotated gold, plus agreement stats.
+
+``export`` flattens one gold ``*.json`` (the ``annotate_llm.py`` output) into a review
+TSV — one row per candidate span with a marked context window and empty ``verdict`` /
+``fix_entity_id`` / ``fix_direction`` columns — for spreadsheet review. With ``--other``
+(a second annotator's file), each row also shows the other annotator's call
+(``other_entity_id``, ``other_label``, ``other_direction``), spans only the second
+annotator proposed are appended as ``origin=other`` rows, and the command prints Cohen's
+κ.
+
+Agreement is read in the reference inventory's tiers 0 and 1
+(:mod:`pelinker.eval.reference`): ids are compared after folding converse members onto
+their canonical member and, with ``--equivalences``, along the KB-implied identities.
+Two annotators who call one mention by two names of the same relation therefore agree.
+``agrees_direction`` is filled only where the relation agrees, and a symmetric relation
+agrees in any direction. κ is reported raw and folded.
+
+The second annotator may cover fewer documents than the first (a slice, or documents it
+could not answer), so two things follow: rows on documents it never saw read
+``not-covered`` rather than an empty disagreement, and κ is computed over the shared
+documents alone.
+
+``import`` merges the filled sheet back: ``verdict`` ∈ ``accept`` / ``reject`` / ``fix``
+(with the ``fix_*`` columns), everything else is an error so nothing is silently kept.
+Accepted/fixed hits become ``source="human"``. Accepting an ``origin=other`` row needs
+``--other`` as well, since the hit itself (and its argument spans) lives in that file —
+without it, verified gold could never exceed the first annotator's recall.
+
+``agreement`` prints κ between two gold files without exporting a sheet.
+
+Usage:
+
+    uv run python run/eval/gold_review_sheet.py export \
+        --gold gold.llm-a.json --other gold.llm-b.json --kb-csv-path <kb> \
+        --equivalences data/curated/equivalences.csv --output review.tsv
+    uv run python run/eval/gold_review_sheet.py import \
+        --gold gold.llm-a.json --sheet review.tsv --other gold.llm-b.json \
+        --verified-by <name> --output gold.verified.json
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+
+import click
+import pandas as pd
+from sklearn.metrics import cohen_kappa_score
+from pelinker.core.paths import ExpandedPath
+from pelinker.eval.reference import fold_map, folded_symmetric_ids, load_equivalences
+
+logger = logging.getLogger(__name__)
+
+_CONTEXT_CHARS = 60
+_VERDICTS = {"accept", "reject", "fix"}
+
+
+def load_gold(path: str | Path) -> list[dict]:
+    data = json.loads(Path(path).read_text(encoding="utf-8"))
+    return data if isinstance(data, list) else [data]
+
+
+def _context(text: str, a: int, b: int) -> str:
+    lo = max(0, a - _CONTEXT_CHARS)
+    hi = min(len(text), b + _CONTEXT_CHARS)
+    return (
+        (text[lo:a] + "【" + text[a:b] + "】" + text[b:hi])
+        .replace("\t", " ")
+        .replace("\n", " ")
+    )
+
+
+def _spans_overlap(a1: int, b1: int, a2: int, b2: int) -> bool:
+    return a1 < b2 and a2 < b1
+
+
+def shared_doc_ids(docs_a: list[dict], docs_b: list[dict]) -> set:
+    """Doc ids both annotators actually saw.
+
+    The second annotator normally covers only the ``double`` slice. Scoring agreement over
+    every document of the first would count each of its hits on an unannotated document as
+    a disagreement, driving κ toward 0 for a reason that has nothing to do with agreement.
+    """
+    return {d["doc_id"] for d in docs_a} & {d["doc_id"] for d in docs_b}
+
+
+def align_hits(
+    docs_a: list[dict], docs_b: list[dict], *, restrict_to_shared: bool = False
+) -> list[tuple[dict | None, dict | None]]:
+    """Greedy span alignment of two annotators' hits per document (by doc_id).
+
+    Pairs are ``(hit_a, hit_b)`` with either side ``None`` when only one annotator marked
+    that span. B-only spans are emitted last, per document, so a caller that only wants
+    A's rows can ignore them by skipping ``hit_a is None``.
+    """
+    shared = shared_doc_ids(docs_a, docs_b) if restrict_to_shared else None
+    b_by_doc = {d["doc_id"]: list(d.get("ground_truth") or []) for d in docs_b}
+    pairs: list[tuple[dict | None, dict | None]] = []
+    for doc in docs_a:
+        if shared is not None and doc["doc_id"] not in shared:
+            continue
+        others = b_by_doc.get(doc["doc_id"], [])
+        used: set[int] = set()
+        for hit in doc.get("ground_truth") or []:
+            match = None
+            for j, other in enumerate(others):
+                if j in used:
+                    continue
+                if _spans_overlap(hit["a"], hit["b"], other["a"], other["b"]):
+                    match = other
+                    used.add(j)
+                    break
+            pairs.append((hit, match))
+        for j, other in enumerate(others):
+            if j not in used:
+                pairs.append((None, other))
+    return pairs
+
+
+def load_fold(
+    kb: pd.DataFrame, equivalences_path: str | None
+) -> tuple[dict[str, str], frozenset[str]]:
+    """The T̂ tier 0+1 fold of ``kb`` and the folded ids of its symmetric relations."""
+    equivalences = (
+        None
+        if equivalences_path is None
+        else load_equivalences(equivalences_path, kb=kb)
+    )
+    fold = fold_map(kb, equivalences)
+    return fold, folded_symmetric_ids(kb, fold)
+
+
+def ids_agree(id_a: object, id_b: object, fold: dict[str, str]) -> bool:
+    a, b = str(id_a), str(id_b)
+    return fold.get(a, a) == fold.get(b, b)
+
+
+def directions_agree(
+    hit: dict, other: dict, fold: dict[str, str], symmetric: frozenset[str]
+) -> bool | None:
+    """Direction agreement on a span both annotators linked to the same relation.
+
+    ``None`` where it is not defined: the relations differ (a direction comparison
+    between two relations says nothing), or a side gave no direction. A symmetric
+    relation has no orientation, so any two directions agree.
+    """
+    if not ids_agree(hit["entity_id"], other["entity_id"], fold):
+        return None
+    relation = fold.get(str(hit["entity_id"]), str(hit["entity_id"]))
+    if relation in symmetric:
+        return True
+    if not hit.get("direction") or not other.get("direction"):
+        return None
+    return str(hit["direction"]) == str(other["direction"])
+
+
+def kappa_report(
+    docs_a: list[dict], docs_b: list[dict], *, fold: dict[str, str] | None = None
+) -> dict:
+    """Cohen's κ on entity id and direction over span-aligned pairs.
+
+    Computed over the documents **both** annotators covered; ``n_docs_shared`` reports how
+    many that was, and a κ over zero shared documents is absent rather than 0.
+
+    An unmatched span enters the entity-id κ as an explicit ``∅`` category on the side that
+    missed it — in both directions, so an annotator that hallucinates spans and one that
+    misses them are penalized alike. Direction κ is computed over pairs where both sides
+    matched and gave a direction.
+
+    With ``fold`` (the T̂ tier 0+1 map), ``kappa_entity_id_folded`` is added: the same κ
+    after both sides' ids are folded, so naming one relation two ways is not a
+    disagreement.
+    """
+    pairs = align_hits(docs_a, docs_b, restrict_to_shared=True)
+    ids_a = [("∅" if h is None else str(h["entity_id"])) for h, _ in pairs]
+    ids_b = [("∅" if o is None else str(o["entity_id"])) for _, o in pairs]
+    report: dict = {
+        "n_docs_shared": len(shared_doc_ids(docs_a, docs_b)),
+        "n_pairs": len(pairs),
+        "n_span_matched": sum(1 for h, o in pairs if h is not None and o is not None),
+        "n_only_a": sum(1 for h, o in pairs if h is not None and o is None),
+        "n_only_b": sum(1 for h, o in pairs if h is None and o is not None),
+    }
+    if len(set(ids_a) | set(ids_b)) > 1 and pairs:
+        report["kappa_entity_id"] = float(cohen_kappa_score(ids_a, ids_b))
+    if fold is not None and pairs:
+        folded_a = [fold.get(i, i) for i in ids_a]
+        folded_b = [fold.get(i, i) for i in ids_b]
+        if len(set(folded_a) | set(folded_b)) > 1:
+            report["kappa_entity_id_folded"] = float(
+                cohen_kappa_score(folded_a, folded_b)
+            )
+    directed = [
+        (str(h.get("direction")), str(o.get("direction")))
+        for h, o in pairs
+        if h is not None and o is not None and h.get("direction") and o.get("direction")
+    ]
+    report["n_direction_pairs"] = len(directed)
+    if directed and len({d for pair in directed for d in pair}) > 1:
+        da, db = zip(*directed)
+        report["kappa_direction"] = float(cohen_kappa_score(list(da), list(db)))
+    return report
+
+
+@click.group()
+def main() -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+
+
+@main.command("export")
+@click.option("--gold", required=True, type=ExpandedPath(exists=True))
+@click.option(
+    "--other",
+    default=None,
+    type=ExpandedPath(exists=True),
+    help="Second annotator's gold over the same docs (adds agreement columns + κ).",
+)
+@click.option("--kb-csv-path", required=True, type=ExpandedPath(exists=True))
+@click.option(
+    "--equivalences",
+    default=None,
+    type=ExpandedPath(exists=True),
+    help="KB-implied identities (T̂ tier 1) folded before agreement is read.",
+)
+@click.option("--output", required=True, type=ExpandedPath())
+def export_cmd(
+    gold: str,
+    other: str | None,
+    kb_csv_path: str,
+    equivalences: str | None,
+    output: str,
+) -> None:
+    if Path(output).exists():
+        # A sheet on disk may hold hours of verdicts; a re-export must go to a new file.
+        raise click.ClickException(
+            f"{output} exists; export to a new path rather than overwrite a review sheet"
+        )
+    docs = load_gold(gold)
+    kb = pd.read_csv(kb_csv_path)
+    label_of = dict(zip(kb["entity_id"].astype(str), kb["label"].astype(str)))
+    fold, symmetric = load_fold(kb, equivalences)
+
+    text_of = {doc["doc_id"]: doc["text"] for doc in docs}
+
+    def _row(doc_id, hit: dict, *, origin: str) -> dict:
+        text = text_of[doc_id]
+        return {
+            "doc_id": doc_id,
+            "origin": origin,
+            "itext": hit.get("itext"),
+            "a": hit["a"],
+            "b": hit["b"],
+            "context": _context(text, hit["a"], hit["b"]),
+            "surface": hit.get("surface", text[hit["a"] : hit["b"]]),
+            "entity_id": hit["entity_id"],
+            "label": label_of.get(str(hit["entity_id"]), ""),
+            "direction": hit.get("direction") or "",
+            # "False" marks a paraphrase: the surface does not use the label's wording.
+            "surface_anchored": hit.get("surface_anchored", ""),
+            "confidence": hit.get("confidence", ""),
+            "annotator": hit.get("annotator", ""),
+        }
+
+    rows: list[dict] = []
+    if other is None:
+        for doc in docs:
+            for hit in doc.get("ground_truth") or []:
+                rows.append(_row(doc["doc_id"], hit, origin="gold"))
+    else:
+        docs_b = load_gold(other)
+        shared = shared_doc_ids(docs, docs_b)
+        b_doc_of: dict[int, int] = {}
+        for d in docs_b:
+            for h in d.get("ground_truth") or []:
+                b_doc_of[id(h)] = d["doc_id"]
+        for doc in docs:
+            doc_pairs = align_hits([doc], docs_b)
+            for hit, match in doc_pairs:
+                if hit is None:
+                    # A span only the second annotator marked. Without these rows the
+                    # verified gold can never exceed the first annotator's recall, and the
+                    # adjudicator is never shown what they missed.
+                    assert match is not None
+                    row = _row(b_doc_of[id(match)], match, origin="other")
+                    row["other_entity_id"] = ""
+                    row["other_label"] = ""
+                    row["other_direction"] = ""
+                    row["agrees"] = ""
+                    row["agrees_direction"] = ""
+                else:
+                    row = _row(doc["doc_id"], hit, origin="gold")
+                    if doc["doc_id"] not in shared:
+                        # The second annotator never saw this document; an empty
+                        # comparison here means "not covered", not "disagreed".
+                        row["other_entity_id"] = "not-covered"
+                        row["other_label"] = "not-covered"
+                        row["other_direction"] = "not-covered"
+                        row["agrees"] = "not-covered"
+                        row["agrees_direction"] = "not-covered"
+                    elif match is None:
+                        row["other_entity_id"] = ""
+                        row["other_label"] = ""
+                        row["other_direction"] = ""
+                        row["agrees"] = ""
+                        row["agrees_direction"] = ""
+                    else:
+                        row["other_entity_id"] = match["entity_id"]
+                        row["other_label"] = label_of.get(str(match["entity_id"]), "")
+                        row["other_direction"] = match.get("direction") or ""
+                        row["agrees"] = str(
+                            ids_agree(hit["entity_id"], match["entity_id"], fold)
+                        )
+                        same_direction = directions_agree(hit, match, fold, symmetric)
+                        row["agrees_direction"] = (
+                            "" if same_direction is None else str(same_direction)
+                        )
+                rows.append(row)
+
+    for row in rows:
+        row["verdict"] = ""
+        row["fix_entity_id"] = ""
+        row["fix_direction"] = ""
+
+    pd.DataFrame(rows).to_csv(output, sep="\t", index=False)
+    logger.info(
+        "Wrote %d review rows to %s (%d proposed by the second annotator only)",
+        len(rows),
+        output,
+        sum(1 for r in rows if r["origin"] == "other"),
+    )
+    if other is not None:
+        report = kappa_report(docs, load_gold(other), fold=fold)
+        logger.info("Agreement: %s", json.dumps(report, indent=1))
+
+
+@main.command("import")
+@click.option("--gold", required=True, type=ExpandedPath(exists=True))
+@click.option("--sheet", required=True, type=ExpandedPath(exists=True))
+@click.option(
+    "--other",
+    default=None,
+    type=ExpandedPath(exists=True),
+    help=(
+        "The second annotator's gold, required when the sheet carries origin=other rows; "
+        "accepted ones are taken from here with their argument spans intact."
+    ),
+)
+@click.option("--verified-by", required=True)
+@click.option("--output", required=True, type=ExpandedPath())
+def import_cmd(
+    gold: str, sheet: str, other: str | None, verified_by: str, output: str
+) -> None:
+    docs = load_gold(gold)
+    sheet_df = pd.read_csv(sheet, sep="\t", dtype=str).fillna("")
+
+    verdicts: dict[tuple[int, int, int], tuple[str, str, str]] = {}
+    accepted_other: list[tuple[int, int, int]] = []
+    for _, row in sheet_df.iterrows():
+        verdict = row["verdict"].strip().lower()
+        if verdict not in _VERDICTS:
+            raise click.ClickException(
+                f"doc_id={row['doc_id']} span=({row['a']},{row['b']}): verdict must be "
+                f"one of {sorted(_VERDICTS)}, got {row['verdict']!r} — every row needs "
+                "an explicit decision"
+            )
+        key = (int(row["doc_id"]), int(row["a"]), int(row["b"]))
+        verdicts[key] = (
+            verdict,
+            row["fix_entity_id"].strip(),
+            row["fix_direction"].strip(),
+        )
+        # Legacy sheets predate the column; everything in them came from --gold.
+        origin = row["origin"].strip() if "origin" in sheet_df.columns else "gold"
+        if origin == "other" and verdict != "reject":
+            accepted_other.append(key)
+
+    if accepted_other and other is None:
+        raise click.ClickException(
+            f"{len(accepted_other)} accepted rows were proposed by the second annotator "
+            "(origin=other); pass --other with that annotator's gold file so their spans "
+            "can be carried over"
+        )
+
+    other_hits: dict[tuple[int, int, int], dict] = {}
+    if other is not None:
+        for doc in load_gold(other):
+            for hit in doc.get("ground_truth") or []:
+                other_hits[(int(doc["doc_id"]), int(hit["a"]), int(hit["b"]))] = hit
+
+    n_kept = n_fixed = n_rejected = n_adopted = 0
+    # Verdict counts split by surface type: the correction rate is reported per slice.
+    by_slice: dict[str, dict[str, int]] = {}
+
+    def _count(hit: dict, verdict: str) -> None:
+        anchored = hit.get("surface_anchored")
+        name = (
+            "unknown"
+            if anchored is None
+            else ("anchored" if anchored else "paraphrase")
+        )
+        slot = by_slice.setdefault(name, {"accept": 0, "fix": 0, "reject": 0})
+        slot[verdict] += 1
+
+    def _apply(hit: dict, verdict: str, fix_id: str, fix_dir: str) -> dict:
+        if verdict == "fix":
+            if fix_id:
+                hit["entity_id"] = fix_id
+            if fix_dir:
+                hit["direction"] = fix_dir
+        hit["source"] = "human"
+        hit["annotator"] = verified_by
+        hit["verdict"] = verdict
+        return hit
+
+    for doc in docs:
+        doc_id = int(doc["doc_id"])
+        kept = []
+        for hit in doc.get("ground_truth") or []:
+            key = (doc_id, int(hit["a"]), int(hit["b"]))
+            if key not in verdicts:
+                raise click.ClickException(f"sheet is missing a row for {key}")
+            verdict, fix_id, fix_dir = verdicts[key]
+            _count(hit, verdict)
+            if verdict == "reject":
+                n_rejected += 1
+                continue
+            n_fixed += verdict == "fix"
+            n_kept += verdict == "accept"
+            kept.append(_apply(hit, verdict, fix_id, fix_dir))
+
+        for key in accepted_other:
+            if key[0] != doc_id:
+                continue
+            hit = other_hits.get(key)
+            if hit is None:
+                raise click.ClickException(
+                    f"--other has no hit for the accepted origin=other row {key}"
+                )
+            verdict, fix_id, fix_dir = verdicts[key]
+            n_adopted += 1
+            kept.append(_apply(dict(hit), verdict, fix_id, fix_dir))
+
+        kept.sort(key=lambda h: (int(h["a"]), int(h["b"])))
+        doc["ground_truth"] = kept
+
+    Path(output).write_text(
+        json.dumps(docs, ensure_ascii=False, indent=1), encoding="utf-8"
+    )
+    reviewed = n_kept + n_fixed + n_rejected
+    summary = {
+        "verified_by": verified_by,
+        "reviewed_candidates": reviewed,
+        "accepted": n_kept,
+        "fixed": n_fixed,
+        "rejected": n_rejected,
+        "adopted_from_second_annotator": n_adopted,
+        # The share of first-annotator candidates a curator changed or removed.
+        "correction_rate": round((n_fixed + n_rejected) / reviewed, 4)
+        if reviewed
+        else None,
+        "by_slice": by_slice,
+    }
+    summary_path = Path(output).with_suffix(".verification.json")
+    summary_path.write_text(json.dumps(summary, indent=1), encoding="utf-8")
+    logger.info(
+        "Verified gold: %d accepted, %d fixed, %d rejected, %d adopted from the second "
+        "annotator → %s",
+        n_kept,
+        n_fixed,
+        n_rejected,
+        n_adopted,
+        output,
+    )
+
+
+@main.command("agreement")
+@click.option("--gold", required=True, type=ExpandedPath(exists=True))
+@click.option("--other", required=True, type=ExpandedPath(exists=True))
+@click.option(
+    "--kb-csv-path",
+    default=None,
+    type=ExpandedPath(exists=True),
+    help="Pairs KB; adds the κ folded in T̂ tier 0 (and tier 1 with --equivalences).",
+)
+@click.option("--equivalences", default=None, type=ExpandedPath(exists=True))
+def agreement_cmd(
+    gold: str, other: str, kb_csv_path: str | None, equivalences: str | None
+) -> None:
+    if equivalences is not None and kb_csv_path is None:
+        raise click.ClickException("--equivalences needs --kb-csv-path")
+    fold = None
+    if kb_csv_path is not None:
+        fold, _ = load_fold(pd.read_csv(kb_csv_path), equivalences)
+    report = kappa_report(load_gold(gold), load_gold(other), fold=fold)
+    click.echo(json.dumps(report, indent=1))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/eval/prompts/annotate_predicates.md b/run/eval/prompts/annotate_predicates.md
new file mode 100644
index 0000000..ad898c9
--- /dev/null
+++ b/run/eval/prompts/annotate_predicates.md
@@ -0,0 +1,71 @@
+# Predicate-mention annotation prompt
+
+Slots: `{kb_table}` (a numbered catalog, one relation per line, rendered by
+`pelinker.eval.kb_prompt`: `N. "label" | def: … | symmetric | converse wording: "…"`),
+`{abstract}` (the document text). The model returns **labels, not ids**: opaque ids
+carry no meaning for a language model, and asking it to transcribe one turns a semantic
+judgement into a copying task that silently fails. Ids are resolved afterwards by
+`annotate_llm.py`, which rejects any label it cannot match.
+
+Only *canonical* relation labels are offered. The KB stores both members of a converse
+pair, so a passive mention could otherwise be encoded two equally valid ways — "regulated
+by"/forward or "regulates"/inverse — and that ambiguity is what put contradictory labels
+on the same span in the pipeline's own weak supervision. Here the relation and its
+orientation are annotated separately: pick the canonical relation, then say which way it
+runs. Relations marked `symmetric` have no orientation; the decoder sets their direction
+to "symmetric" whatever the model answers, and flags the hit when it had to.
+
+## System
+
+You are an expert biomedical curator annotating predicate mentions in scientific
+abstracts. A predicate mention is a word or short phrase (1–4 tokens) expressing a
+relation between entities — e.g. "activates", "is required for", "binds", "leads to".
+
+You are given a fixed list of relations. For every predicate mention in the abstract that
+expresses one of them, produce one annotation record. Ignore relations that no listed
+entry covers. Do not annotate entity names, only relation expressions.
+
+Relations — a numbered list, one per line. Each entry is
+`N. "label" | def: definition | symmetric | converse wording: "other surface form"`, where
+`def`, `symmetric` and `converse wording` may be absent. Only the quoted `label` is a valid answer; the number is
+for reference only and the converse wording is never itself an answer.
+
+{kb_table}
+
+Rules:
+
+1. `surface` must be quoted VERBATIM from the abstract — exact characters, including
+   case. Never paraphrase, never normalize.
+2. `occurrence` is the 1-based index of that exact surface string within the abstract
+   (1 = first occurrence). Annotate each distinct occurrence separately.
+3. `label` must be one of the quoted relation labels above, copied exactly and
+   without the surrounding quotes or the item number. Always use the listed label even
+   when the text uses the converse wording — the converse form is recorded in
+   `direction`, never by switching to a different label.
+4. `direction` describes how the relation runs in this sentence, relative to the listed
+   label's own reading:
+   - "forward" — the sentence reads the same way as the label (X activates Y ⇒
+     "activates").
+   - "inverse" — the sentence reads the other way round: passive voice or the converse
+     wording (Y is activated by X ⇒ still label "activates", direction "inverse").
+   - "symmetric" — the relation has no orientation (X interacts with Y). Always use
+     "symmetric" for relations marked `symmetric` in the list, whatever the voice.
+   - "na" — orientation is not determinable from the sentence.
+5. `subject_text` / `object_text`: the verbatim surface strings of the relation's
+   arguments, assigned by their roles **under the label's forward reading** — so for an
+   "inverse" mention, `subject_text` is the argument that would be the subject if the
+   sentence were rewritten in the label's own wording. Use null when not identifiable.
+6. `confidence`: your confidence in the label assignment, 0.0–1.0.
+7. When several relations could fit, pick the most specific one and lower `confidence`
+   accordingly.
+8. If the abstract contains no covered predicate mention, return an empty list.
+
+Return ONLY a JSON array (no prose, no code fences), each element:
+
+{"surface": str, "occurrence": int, "label": str, "direction": "forward"|"inverse"|"symmetric"|"na", "subject_text": str|null, "object_text": str|null, "confidence": float}
+
+## User
+
+Abstract:
+
+{abstract}
diff --git a/run/eval/run_baselines.py b/run/eval/run_baselines.py
new file mode 100644
index 0000000..992d950
--- /dev/null
+++ b/run/eval/run_baselines.py
@@ -0,0 +1,282 @@
+"""Score the linker and the reference baselines on a verified gold file.
+
+Runs each selected system in both regimes where it applies (end-to-end span+link, and
+linking-only over gold spans) through the shared harness, so quality and runtime come
+from one pass. Results are written as JSON + CSV to ``--report-dir``; keep that directory
+outside this repository — measured numbers belong with the measurement writeup, not in
+the package.
+
+Systems:
+
+- ``lexical`` — lemma match to KB labels (both regimes)
+- ``encoder`` — sentence-encoder cosine top-1 over KB label+description (linking-only;
+  end-to-end reuses the lexical span proposer)
+- ``llm`` — LLM constrained choice over the KB (linking-only; needs the ``eval`` extra
+  and a provider credential; responses are cached under ``--llm-cache-dir``). Must not
+  be the model that annotated the gold, or the comparison is circular.
+- ``linker`` — a fitted PELinker artifact (both regimes), with the KB-out → KB-in id
+  bridge applied so entity accuracy is comparable
+
+Ids are scored in **canonical space**: gold carries the canonical member of each converse
+pair, so every system's answer is folded the same way before comparison. The KB therefore
+has to be the pairs KB (``run/preprocessing/derive_inverse_pairs.py``). Direction is not
+scored here — the baselines assign an id and nothing else.
+
+Usage:
+
+    uv run python run/eval/run_baselines.py \
+        --gold <workdir>/gold/gold.verified.json \
+        --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+        --report-dir <workdir>/eval-runs/<run-name> \
+        --systems lexical,encoder \
+        --model-path <models>/pelinker.pubmedbert.1
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+
+import click
+import pandas as pd
+import spacy
+
+from pelinker.eval.baselines import (
+    EncoderKnnBaseline,
+    LexicalLemmaBaseline,
+    LlmLinkerBaseline,
+)
+from pelinker.eval.kb import canonical_id_map
+from pelinker.eval.llm import DEFAULT_MODEL, DEFAULT_PROVIDER, PROVIDERS
+from pelinker.eval.harness import (
+    evaluate_end_to_end,
+    evaluate_linking_only,
+    load_gold_docs,
+)
+from pelinker.core.paths import ExpandedPath
+
+logger = logging.getLogger(__name__)
+
+_ALL_SYSTEMS = ("lexical", "encoder", "llm", "linker")
+
+
+def _linker_predict_fn(linker, thr_score: float):
+    def predict(texts: list[str]) -> list[dict]:
+        result = linker.predict(texts, threshold=thr_score)
+        return [dict(row) for row in (result.entities or [])]
+
+    return predict
+
+
+def _linker_link_fn(linker, thr_score: float):
+    """Linking-only: predict over the span's own text and take the covering row."""
+
+    def link(text: str, a: int, b: int) -> str | None:
+        result = linker.predict([text], threshold=thr_score)
+        best: tuple[int, str] | None = None
+        for row in result.entities or []:
+            ra, rb = int(row["a"]), int(row["b"])
+            if ra < b and a < rb:  # overlaps the gold span
+                overlap = min(b, rb) - max(a, ra)
+                eid = row.get("entity_id_predicted")
+                if eid is not None and (best is None or overlap > best[0]):
+                    best = (overlap, str(eid))
+        return None if best is None else best[1]
+
+    return link
+
+
+@click.command()
+@click.option("--gold", required=True, type=ExpandedPath(exists=True))
+@click.option("--kb-csv-path", required=True, type=ExpandedPath(exists=True))
+@click.option("--report-dir", required=True, type=ExpandedPath())
+@click.option("--systems", default="lexical,encoder", show_default=True)
+@click.option("--nlp-model", default="en_core_web_lg", show_default=True)
+@click.option(
+    "--encoder-model",
+    default="neuml/pubmedbert-base-embeddings",
+    show_default=True,
+)
+@click.option(
+    "--llm-provider",
+    type=click.Choice(PROVIDERS),
+    default=DEFAULT_PROVIDER,
+    show_default=True,
+)
+@click.option("--llm-model", default=DEFAULT_MODEL, show_default=True)
+@click.option("--llm-cache-dir", default=None, type=ExpandedPath(file_okay=False))
+@click.option(
+    "--model-path",
+    default=None,
+    type=ExpandedPath(),
+    help="Fitted linker artifact (system: linker).",
+)
+@click.option("--thr-score", default=None, type=float)
+@click.option("--match-mode", default="overlap", show_default=True)
+def main(
+    gold: str,
+    kb_csv_path: str,
+    report_dir: str,
+    systems: str,
+    nlp_model: str,
+    encoder_model: str,
+    llm_provider: str,
+    llm_model: str,
+    llm_cache_dir: str | None,
+    model_path: str | None,
+    thr_score: float | None,
+    match_mode: str,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    wanted = [s.strip() for s in systems.split(",") if s.strip()]
+    unknown = set(wanted) - set(_ALL_SYSTEMS)
+    if unknown:
+        raise click.ClickException(
+            f"unknown systems {sorted(unknown)}; choose from {list(_ALL_SYSTEMS)}"
+        )
+
+    docs = load_gold_docs(gold)
+    n_spans = sum(len(d.spans) for d in docs)
+    logger.info("Gold: %d docs, %d spans", len(docs), n_spans)
+    kb = pd.read_csv(kb_csv_path)
+    try:
+        # Gold is annotated in canonical space. The baselines choose from the whole KB,
+        # which still holds both members of each converse pair, so their ids are folded
+        # onto the canonical member before comparison — otherwise a correct "regulated by"
+        # link scores as an error and the baselines measure the vocabulary's redundancy.
+        canonical = canonical_id_map(kb, kb_csv_path=kb_csv_path)
+    except ValueError as err:
+        raise click.ClickException(str(err)) from err
+
+    runs = []
+
+    if {"lexical", "encoder", "llm"} & set(wanted):
+        nlp = spacy.load(nlp_model)
+        lexical = LexicalLemmaBaseline(kb, nlp)
+
+    if "lexical" in wanted:
+        runs.append(
+            evaluate_end_to_end(
+                lexical.predict,
+                docs,
+                system="lexical",
+                match_mode=match_mode,
+                canonicalize=canonical,
+            )
+        )
+        runs.append(
+            evaluate_linking_only(
+                lexical.link, docs, system="lexical", canonicalize=canonical
+            )
+        )
+
+    if "encoder" in wanted:
+        enc = EncoderKnnBaseline(kb, model_name=encoder_model)
+        runs.append(
+            evaluate_linking_only(
+                enc.link, docs, system="encoder_knn", canonicalize=canonical
+            )
+        )
+
+        # End-to-end: lexical spans, encoder ids — isolates the id decision.
+        def encoder_predict(texts: list[str]) -> list[dict]:
+            rows = []
+            for itext, text in enumerate(texts):
+                for a, b, _ in lexical.proposer.propose(text):
+                    rows.append(
+                        {
+                            "itext": itext,
+                            "a": a,
+                            "b": b,
+                            "entity_id_predicted": enc.link(text, a, b),
+                        }
+                    )
+            return rows
+
+        runs.append(
+            evaluate_end_to_end(
+                encoder_predict,
+                docs,
+                system="encoder_knn",
+                match_mode=match_mode,
+                canonicalize=canonical,
+            )
+        )
+
+    if "llm" in wanted:
+        cache = Path(llm_cache_dir or (Path(report_dir) / ".llm_cache"))
+        llm = LlmLinkerBaseline(
+            kb, model=llm_model, cache_dir=cache, provider=llm_provider
+        )
+        runs.append(
+            evaluate_linking_only(
+                llm.link,
+                docs,
+                system=f"llm:{llm_provider}:{llm_model}",
+                canonicalize=canonical,
+            )
+        )
+
+    if "linker" in wanted:
+        if model_path is None:
+            raise click.ClickException("--model-path is required for system 'linker'")
+        from pelinker.kb.kb_out import kb_out_to_kb_in_map
+        from pelinker.model import DEFAULT_CLUSTER_MEMBERSHIP_THRESHOLD, Linker
+
+        linker = Linker.load(model_path)
+        thr = (
+            thr_score if thr_score is not None else DEFAULT_CLUSTER_MEMBERSHIP_THRESHOLD
+        )
+        bridge = (
+            kb_out_to_kb_in_map(linker.kb_out_catalog)
+            if linker.kb_out_catalog is not None
+            else None
+        )
+        if not bridge:
+            logger.warning(
+                "No KB-out → KB-in id bridge on this artifact; entity accuracy will be "
+                "undefined (see the catalog's kb_in provenance block)."
+            )
+        runs.append(
+            evaluate_end_to_end(
+                _linker_predict_fn(linker, thr),
+                docs,
+                system="pelinker",
+                match_mode=match_mode,
+                predicted_id_to_kb_in=bridge,
+                canonicalize=canonical,
+            )
+        )
+
+        runs.append(
+            evaluate_linking_only(
+                _linker_link_fn(linker, thr),
+                docs,
+                system="pelinker",
+                predicted_id_to_kb_in=bridge,
+                canonicalize=canonical,
+            )
+        )
+
+    out = Path(report_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    payload = {
+        "gold_path": str(gold),
+        "kb_csv_path": str(kb_csv_path),
+        "n_docs": len(docs),
+        "n_gold_spans": n_spans,
+        "match_mode": match_mode,
+        "runs": [r.to_jsonable() for r in runs],
+    }
+    (out / "baseline_results.json").write_text(json.dumps(payload, indent=1))
+    pd.DataFrame([r.to_jsonable() for r in runs]).to_csv(
+        out / "baseline_results.csv", index=False
+    )
+    for r in runs:
+        logger.info("%s", json.dumps(r.to_jsonable()))
+    logger.info("Wrote %d runs to %s", len(runs), out)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/eval/sample_gold.py b/run/eval/sample_gold.py
new file mode 100644
index 0000000..bf6ae52
--- /dev/null
+++ b/run/eval/sample_gold.py
@@ -0,0 +1,548 @@
+"""Draw — and grow — the stratified gold-annotation sample from a held-out abstract table.
+
+Input: a TSV(.gz) with columns ``ids`` (a stringified record carrying doi / mag /
+openalex / pmcid / pmid), ``publication_year``, ``summary``. By default only
+**pmid-bearing** documents are eligible (``--require-pmid``): the gold set is cited in a
+biomedical paper and a PubMed id is the identifier a reader resolves without help.
+
+**1. Eligibility** (:mod:`pelinker.eval.sample_quality`). A document is drawn only if it
+is a plausible English research abstract — publication year, length and sentence bounds,
+English function-word share — not an exact duplicate, and absent from the fit corpus
+(``--exclude-corpus``, matched on ``mag`` and on text hash). Every dropped row is counted
+by reason in the sampling report.
+
+**2. Mentions** (:class:`pelinker.text.mentions.MentionDetector`). Each eligible abstract
+is parsed and its KB mentions detected with the *same* definition stage (A) of the fit
+uses: verb-predicate labels from the dependency parse, one label per verb, the rest on
+lemma windows, one label per site. A raw lemma-window count would mostly measure abstract
+length, because nouns ("control", "increase") match labels too.
+
+**3. Strata** = publication-year tercile × verb-mention density (``none`` / ``low`` /
+``high``) × ``tail`` — whether the abstract holds a detected label outside the pool's
+``--tail-top-k`` most frequent ones. Tail strata are over-allocated by ``--tail-weight``
+so rare relations reach the gold set sooner; the draw stays random *within* each stratum
+and every document records its inclusion probability ``p_incl``, so rates can be
+reweighted to the pool. Abstracts with no detected mention are drawn as
+``--none-fraction`` negative controls (they measure false positives, not "text without
+relations").
+
+**4. Growth.** ``--extend`` appends a new batch to an existing sample: already-drawn
+documents are excluded, ``doc_id`` values stay stable (row index of the input table),
+roles are assigned per batch in the ``--n-primary`` / ``--n-double`` / ``--n-reserve``
+proportions, and ``sample_texts.jsonl`` is appended, never rewritten. Grow until the
+coverage report (``audit_sample.py``) stops gaining labels.
+
+Outputs, under ``--output-dir``:
+
+- ``sample_manifest.csv`` — one row per drawn abstract: ``doc_id``, ``doc_uid``, every
+  identifier, ``publication_year``, ``n_chars``, ``n_mentions``, ``n_verb_mentions``,
+  ``labels_detected`` (``|``-joined), ``stratum``, ``p_incl``, ``batch``, ``role``,
+  ``text_sha1``.
+- ``sample_texts.jsonl`` — ``{"doc_id", "doc_uid", "ids", "text"}`` per line.
+- ``sampling_report.batch<k>.json`` — gate counts by reason, training overlap, pool and
+  stratum sizes, allocation.
+- ``pool_mentions.csv.gz`` — detected mentions of the whole eligible pool, reused by
+  ``--extend`` while the KB and the gate are unchanged.
+
+Usage:
+
+    uv run python run/eval/sample_gold.py \
+        --input-text-table-path <corpus>.tsv.gz \
+        --exclude-corpus <fit corpus>.tsv.gz \
+        --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+        --output-dir <workdir>/gold
+
+    # later: add a batch of 120 (80 / 20 / 20)
+    uv run python run/eval/sample_gold.py ... --output-dir <workdir>/gold --extend \
+        --n-primary 80 --n-double 20 --n-reserve 20
+"""
+
+from __future__ import annotations
+
+import dataclasses
+import hashlib
+import json
+import logging
+import re
+from pathlib import Path
+
+import click
+import numpy as np
+import pandas as pd
+import spacy
+from pelinker.core.paths import ExpandedPath
+from pelinker.eval import sample_quality as sq
+from pelinker.text.mentions import DETECTOR_VERSION, MentionDetector
+from pelinker.text.tokenize import tokens_from_doc
+
+logger = logging.getLogger(__name__)
+
+_PMID_RE = re.compile(r"pubmed\.ncbi\.nlm\.nih\.gov/(\d+)")
+_ID_PATTERNS: dict[str, re.Pattern[str]] = {
+    "doi": re.compile(r"doi=(?:'([^']*)'|\"([^\"]*)\"|None)"),
+    "mag": re.compile(r"mag=(?:'([^']*)'|\"([^\"]*)\"|(\d+)|None)"),
+    "openalex": re.compile(r"openalex=(?:'([^']*)'|\"([^\"]*)\"|None)"),
+    "pmcid": re.compile(r"pmcid=(?:'([^']*)'|\"([^\"]*)\"|None)"),
+    "pmid": re.compile(r"pmid=(?:'([^']*)'|\"([^\"]*)\"|None)"),
+}
+
+ID_FIELDS: tuple[str, ...] = ("pmid", "doi", "openalex", "mag", "pmcid")
+"""Identifier precedence for ``doc_uid``.
+
+``pmid`` leads because the gold set is drawn from pmid-bearing documents only (see
+``--require-pmid``): it is the identifier biomedical readers resolve without help. The
+rest are recorded too — ``mag`` in particular is what the fit corpus is keyed on, so it
+is what a train/test overlap check compares against.
+"""
+
+
+def extract_identifiers(ids_field: object) -> dict[str, str | None]:
+    """Parse every identifier out of the stringified ``Row(...)`` record.
+
+    The corpus stores one field holding ``doi``/``mag``/``openalex``/``pmcid``/``pmid``,
+    any of which may be ``None``. A bare pmid URL is reduced to its numeric id so the
+    value is comparable with the pmid form used elsewhere.
+    """
+    text = str(ids_field)
+    out: dict[str, str | None] = {}
+    for name, pattern in _ID_PATTERNS.items():
+        match = pattern.search(text)
+        value = None
+        if match is not None:
+            value = next((g for g in match.groups() if g), None)
+        if value is not None:
+            value = value.strip() or None
+        if name == "pmid" and value is not None:
+            numeric = _PMID_RE.search(value)
+            value = numeric.group(1) if numeric else value
+        out[name] = value
+    return out
+
+
+def document_uid(identifiers: dict[str, str | None]) -> str | None:
+    """First available identifier in :data:`ID_FIELDS` precedence, or ``None``."""
+    for field in ID_FIELDS:
+        value = identifiers.get(field)
+        if value:
+            return value
+    return None
+
+
+DENSITY_NONE = "none"
+MANIFEST_COLUMNS = [
+    "doc_id",
+    "doc_uid",
+    *ID_FIELDS,
+    "publication_year",
+    "n_chars",
+    "n_mentions",
+    "n_verb_mentions",
+    "labels_detected",
+    "stratum",
+    "p_incl",
+    "batch",
+    "role",
+    "text_sha1",
+]
+
+
+def kb_fingerprint(
+    kb_csv_path: str | Path, rules: sq.Eligibility, require_pmid: bool
+) -> str:
+    """What the pool cache depends on: KB bytes, gate, pmid rule, detector version."""
+    h = hashlib.sha1(Path(kb_csv_path).read_bytes())
+    h.update(DETECTOR_VERSION.encode())
+    h.update(json.dumps(dataclasses.asdict(rules), sort_keys=True).encode())
+    h.update(str(require_pmid).encode())
+    return h.hexdigest()
+
+
+def detect_pool_mentions(
+    texts: list[str], detector: MentionDetector, nlp, *, batch_size: int = 64
+) -> pd.DataFrame:
+    """``n_mentions``, ``n_verb_mentions``, ``labels_detected`` per text."""
+    rows = []
+    for doc in nlp.pipe(texts, batch_size=batch_size):
+        mentions = detector.detect(tokens_from_doc(doc))
+        rows.append(
+            {
+                "n_mentions": len(mentions),
+                "n_verb_mentions": sum(m.is_verbal for m in mentions),
+                "labels_detected": "|".join(sorted({m.label for m in mentions})),
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def split_labels(value: object) -> list[str]:
+    if not isinstance(value, str) or not value:
+        return []
+    return value.split("|")
+
+
+def tail_labels(labels_detected: pd.Series, top_k: int) -> frozenset[str]:
+    """Labels outside the ``top_k`` most frequent (by number of abstracts) in the pool."""
+    freq: dict[str, int] = {}
+    for value in labels_detected:
+        for label in split_labels(value):
+            freq[label] = freq.get(label, 0) + 1
+    ranked = sorted(freq, key=lambda k: (-freq[k], k))
+    return frozenset(ranked[top_k:])
+
+
+def assign_strata(df: pd.DataFrame, tail: frozenset[str]) -> pd.Series:
+    """``y<tercile>-<none|low|high>-<tail|common>``; negatives carry no tail part."""
+    year_bin = pd.qcut(
+        df["publication_year"].rank(method="first"), q=3, labels=["y0", "y1", "y2"]
+    ).astype(str)
+    positive = df["n_verb_mentions"] > 0
+    median_pos = df.loc[positive, "n_verb_mentions"].median() if positive.any() else 0
+    density = pd.Series(DENSITY_NONE, index=df.index, dtype=object)
+    density[positive & (df["n_verb_mentions"] <= median_pos)] = "low"
+    density[positive & (df["n_verb_mentions"] > median_pos)] = "high"
+    has_tail = df["labels_detected"].map(
+        lambda v: any(label in tail for label in split_labels(v))
+    )
+    tail_part = np.where(has_tail, "tail", "common")
+    stratum = year_bin + "-" + density
+    return stratum.where(density == DENSITY_NONE, stratum + "-" + tail_part)
+
+
+def allocate(sizes: pd.Series, n: int, weights: pd.Series) -> pd.Series:
+    """Largest-remainder allocation of ``n`` over strata ∝ size × weight, capped at size."""
+    alloc = pd.Series(0, index=sizes.index, dtype=int)
+    remaining = n
+    active = sizes[sizes > 0].index.tolist()
+    while remaining > 0 and active:
+        mass = sizes[active] * weights[active]
+        exact = mass / mass.sum() * remaining
+        take = np.minimum(exact.astype(int), sizes[active] - alloc[active])
+        rem = (exact - exact.astype(int)).sort_values(ascending=False)
+        for name in rem.index:
+            if take.sum() >= remaining:
+                break
+            if alloc[name] + take[name] < sizes[name]:
+                take[name] += 1
+        if take.sum() == 0:
+            break
+        alloc[active] += take
+        remaining -= int(take.sum())
+        active = [s for s in active if alloc[s] < sizes[s]]
+    return alloc
+
+
+def draw_batch(
+    pool: pd.DataFrame,
+    *,
+    n_total: int,
+    none_fraction: float,
+    tail_weight: float,
+    seed: int,
+) -> pd.DataFrame:
+    """Random draw within strata; returns the drawn rows with ``p_incl`` set."""
+    is_none = pool["n_verb_mentions"] == 0
+    none_pool, pos_pool = pool[is_none], pool[~is_none]
+    n_none = min(int(round(n_total * none_fraction)), len(none_pool))
+    n_pos = min(n_total - n_none, len(pos_pool))
+
+    sizes = pos_pool.groupby("stratum").size()
+    weights = pd.Series(
+        [tail_weight if name.endswith("-tail") else 1.0 for name in sizes.index],
+        index=sizes.index,
+    )
+    alloc = allocate(sizes, n_pos, weights)
+
+    parts = []
+    none_sizes = none_pool.groupby("stratum").size()
+    none_alloc = allocate(none_sizes, n_none, pd.Series(1.0, index=none_sizes.index))
+    for sizes_, alloc_, frame in (
+        (none_sizes, none_alloc, none_pool),
+        (sizes, alloc, pos_pool),
+    ):
+        for name, group in frame.groupby("stratum"):
+            k = int(alloc_.get(name, 0))
+            if k <= 0:
+                continue
+            picked = group.sample(n=k, random_state=seed).copy()
+            picked["p_incl"] = k / int(sizes_[name])
+            parts.append(picked)
+    if not parts:
+        return pool.iloc[0:0].assign(p_incl=pd.Series(dtype=float))
+    drawn = pd.concat(parts)
+    return drawn.sample(frac=1.0, random_state=seed)  # shuffle before role assignment
+
+
+def assign_roles(n: int, n_primary: int, n_double: int, n_reserve: int) -> list[str]:
+    """Roles for ``n`` drawn rows in the requested proportions (largest remainder)."""
+    total = n_primary + n_double + n_reserve
+    if total <= 0:
+        raise click.ClickException("role counts must sum to a positive number")
+    quotas = {
+        "primary": n_primary / total * n,
+        "double": n_double / total * n,
+        "reserve": n_reserve / total * n,
+    }
+    counts = {k: int(v) for k, v in quotas.items()}
+    for k in sorted(quotas, key=lambda k: quotas[k] - counts[k], reverse=True)[
+        : n - sum(counts.values())
+    ]:
+        counts[k] += 1
+    return (
+        ["primary"] * counts["primary"]
+        + ["double"] * counts["double"]
+        + ["reserve"] * counts["reserve"]
+    )
+
+
+def load_existing(out: Path) -> pd.DataFrame | None:
+    manifest = out / "sample_manifest.csv"
+    if not manifest.exists():
+        return None
+    return pd.read_csv(manifest, dtype={f: str for f in ID_FIELDS})
+
+
+@click.command()
+@click.option("--input-text-table-path", required=True, type=ExpandedPath(exists=True))
+@click.option("--kb-csv-path", required=True, type=ExpandedPath(exists=True))
+@click.option("--output-dir", required=True, type=ExpandedPath())
+@click.option(
+    "--exclude-corpus",
+    default=None,
+    type=ExpandedPath(exists=True),
+    help="Fit corpus TSV (id column, text column); its documents are never drawn.",
+)
+@click.option("--n-primary", default=200, show_default=True)
+@click.option("--n-double", default=50, show_default=True)
+@click.option("--n-reserve", default=50, show_default=True)
+@click.option(
+    "--none-fraction",
+    default=0.1,
+    show_default=True,
+    help="Share of the batch drawn from abstracts with no detected mention.",
+)
+@click.option("--tail-top-k", default=20, show_default=True)
+@click.option(
+    "--tail-weight",
+    default=2.0,
+    show_default=True,
+    help="Allocation weight of strata holding a rare (tail) label; 1.0 = proportional.",
+)
+@click.option("--min-year", default=sq.Eligibility.min_year, show_default=True)
+@click.option("--min-chars", default=sq.Eligibility.min_chars, show_default=True)
+@click.option("--max-chars", default=sq.Eligibility.max_chars, show_default=True)
+@click.option(
+    "--min-sentences", default=sq.Eligibility.min_sentences, show_default=True
+)
+@click.option(
+    "--min-english-share", default=sq.Eligibility.min_english_share, show_default=True
+)
+@click.option(
+    "--require-pmid/--no-require-pmid",
+    default=True,
+    show_default=True,
+    help=(
+        "Draw only from documents carrying a PubMed id. On by default: the gold set is "
+        "cited in a biomedical paper, and a pmid is the identifier a reader resolves "
+        "without help."
+    ),
+)
+@click.option(
+    "--extend",
+    is_flag=True,
+    help="Append a new batch to the sample already in --output-dir.",
+)
+@click.option("--seed", default=13, show_default=True)
+@click.option("--nlp-model", default="en_core_web_lg", show_default=True)
+@click.option(
+    "--limit",
+    default=None,
+    type=int,
+    help="Only consider the first N corpus rows (smoke tests).",
+)
+def main(
+    input_text_table_path: str,
+    kb_csv_path: str,
+    output_dir: str,
+    exclude_corpus: str | None,
+    n_primary: int,
+    n_double: int,
+    n_reserve: int,
+    none_fraction: float,
+    tail_top_k: int,
+    tail_weight: float,
+    min_year: int,
+    min_chars: int,
+    max_chars: int,
+    min_sentences: int,
+    min_english_share: float,
+    require_pmid: bool,
+    extend: bool,
+    seed: int,
+    nlp_model: str,
+    limit: int | None,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    out = Path(output_dir)
+    existing = load_existing(out)
+    if existing is not None and not extend:
+        raise click.ClickException(
+            f"{out} already holds a sample; pass --extend to add a batch, or choose a "
+            "new --output-dir"
+        )
+    if existing is None and extend:
+        raise click.ClickException(f"--extend: no sample_manifest.csv in {out}")
+    batch = 0 if existing is None else int(existing["batch"].max()) + 1
+    batch_seed = seed + batch
+
+    rules = sq.Eligibility(
+        min_year=min_year,
+        min_chars=min_chars,
+        max_chars=max_chars,
+        min_sentences=min_sentences,
+        min_english_share=min_english_share,
+    )
+    report: dict = {
+        "batch": batch,
+        "seed": batch_seed,
+        "eligibility": dataclasses.asdict(rules),
+    }
+
+    corpus = pd.read_csv(input_text_table_path, sep="\t")
+    required = {"ids", "publication_year", "summary"}
+    if not required.issubset(corpus.columns):
+        raise click.ClickException(
+            f"corpus table must have columns {sorted(required)}, got {list(corpus.columns)}"
+        )
+    if limit is not None:
+        corpus = corpus.head(limit)
+    corpus = corpus.reset_index(drop=True)
+    corpus["doc_id"] = corpus.index
+    identifiers = corpus["ids"].map(extract_identifiers)
+    for field in _ID_PATTERNS:
+        corpus[field] = identifiers.map(lambda ids, f=field: ids[f])
+    corpus["doc_uid"] = identifiers.map(document_uid)
+    corpus["summary"] = corpus["summary"].fillna("").astype(str)
+    report["corpus_rows"] = len(corpus)
+
+    if require_pmid:
+        corpus = corpus.loc[corpus["pmid"].notna()].copy()
+        if len(corpus) == 0:
+            raise click.ClickException(
+                "no corpus rows carry a pmid; pass --no-require-pmid to draw from all "
+                "documents and accept weaker provenance"
+            )
+        report["pmid_rows"] = len(corpus)
+
+    training = sq.load_training_keys(exclude_corpus) if exclude_corpus else None
+    reasons = sq.apply_gate(corpus, rules, training=training)
+    report["gate"] = sq.reason_counts(reasons)
+    report["training_corpus_checked"] = training is not None
+    logger.info("Eligibility gate: %s", report["gate"])
+    pool = corpus.loc[reasons.isna()].copy()
+    pool["text_sha1"] = pool["summary"].map(sq.text_sha1)
+    pool["n_chars"] = pool["summary"].str.len()
+
+    # Mentions for the whole eligible pool, cached while the KB and the gate are unchanged.
+    fingerprint = kb_fingerprint(kb_csv_path, rules, require_pmid)
+    cache_path = out / "pool_mentions.csv.gz"
+    cached = pd.read_csv(cache_path) if cache_path.exists() else None
+    if (
+        cached is not None
+        and (cached["fingerprint"] == fingerprint).all()
+        and set(pool["doc_id"]) <= set(cached["doc_id"])
+    ):
+        detected = cached.set_index("doc_id").drop(columns="fingerprint")
+        logger.info("Reusing pool mentions from %s", cache_path)
+    else:
+        kb = pd.read_csv(kb_csv_path)
+        labels = kb["label"].dropna().astype(str).tolist()
+        symmetric = (
+            frozenset(kb.loc[kb["is_symmetric"].fillna(False).astype(bool), "label"])
+            if "is_symmetric" in kb.columns
+            else frozenset()
+        )
+        nlp = spacy.load(nlp_model)
+        detector = MentionDetector(labels, nlp, symmetric)
+        logger.info(
+            "Detecting mentions in %d eligible abstracts (%d verb labels)",
+            len(pool),
+            len(detector.verb_labels),
+        )
+        detected = detect_pool_mentions(pool["summary"].tolist(), detector, nlp)
+        detected.index = pool["doc_id"].to_numpy()
+        detected.index.name = "doc_id"
+        out.mkdir(parents=True, exist_ok=True)
+        detected.assign(fingerprint=fingerprint).reset_index().to_csv(
+            cache_path, index=False
+        )
+    pool = pool.join(detected, on="doc_id")
+    pool["labels_detected"] = pool["labels_detected"].fillna("")
+
+    tail = tail_labels(pool["labels_detected"], tail_top_k)
+    pool["stratum"] = assign_strata(pool, tail)
+    report["pool_size"] = len(pool)
+    report["pool_strata"] = pool["stratum"].value_counts().sort_index().to_dict()
+    report["tail_labels"] = len(tail)
+
+    if existing is not None:
+        taken = set(existing["doc_uid"].astype(str))
+        pool = pool.loc[~pool["doc_uid"].astype(str).isin(taken)]
+        report["already_drawn"] = len(taken)
+
+    n_total = n_primary + n_double + n_reserve
+    drawn = draw_batch(
+        pool,
+        n_total=n_total,
+        none_fraction=none_fraction,
+        tail_weight=tail_weight,
+        seed=batch_seed,
+    ).copy()
+    drawn["role"] = assign_roles(len(drawn), n_primary, n_double, n_reserve)
+    drawn["batch"] = batch
+    report["drawn"] = len(drawn)
+    report["drawn_strata"] = drawn["stratum"].value_counts().sort_index().to_dict()
+
+    # An annotated document that cannot be cited is not evidence. Refuse rather than
+    # emit a gold row whose provenance is a row index into one local file.
+    unidentified = drawn.loc[drawn["doc_uid"].isna()]
+    if len(unidentified):
+        raise click.ClickException(
+            f"{len(unidentified)} drawn documents carry no identifier "
+            f"({', '.join(ID_FIELDS)}); first doc_ids: "
+            f"{unidentified['doc_id'].head(5).tolist()}"
+        )
+
+    out.mkdir(parents=True, exist_ok=True)
+    manifest = drawn[MANIFEST_COLUMNS]
+    if existing is not None:
+        manifest = pd.concat([existing[MANIFEST_COLUMNS], manifest], ignore_index=True)
+    manifest.to_csv(out / "sample_manifest.csv", index=False)
+    with (out / "sample_texts.jsonl").open("a", encoding="utf-8") as fh:
+        for _, row in drawn.iterrows():
+            record = {
+                "doc_id": int(row["doc_id"]),
+                "doc_uid": row["doc_uid"],
+                "ids": {
+                    field: (None if pd.isna(row[field]) else row[field])
+                    for field in ID_FIELDS
+                },
+                "text": row["summary"],
+            }
+            fh.write(json.dumps(record))
+            fh.write("\n")
+    (out / f"sampling_report.batch{batch}.json").write_text(
+        json.dumps(report, indent=1, default=int), encoding="utf-8"
+    )
+    logger.info(
+        "Batch %d: wrote %d rows (%d primary / %d double / %d reserve) to %s",
+        batch,
+        len(drawn),
+        int((drawn["role"] == "primary").sum()),
+        int((drawn["role"] == "double").sum()),
+        int((drawn["role"] == "reserve").sum()),
+        out,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/loop.embed.kb.corpus.sh b/run/loop.embed.kb.corpus.sh
index f89b6bc..a39e471 100644
--- a/run/loop.embed.kb.corpus.sh
+++ b/run/loop.embed.kb.corpus.sh
@@ -1,13 +1,32 @@
 #!/bin/bash
+# Embed a corpus against the KB for every backbone x layer combination (fit stage A only),
+# writing one mention parquet per combination for the hyperparameter searches.
+#
+# Existing parquets are never overwritten: a previous grid is evidence (for example the
+# "before" arm of a weak-label change), so the run stops on the first file that exists.
+# Write each grid to a new --output-parquet-path directory.
+#
+# Usage:
+#   ./run/loop.embed.kb.corpus.sh --input-text-table-path PATH --output-parquet-path DIR \
+#       --kb-csv-path data/derived/properties.synthesis.2.pairs.csv \
+#       [--models "pubmedbert scibert"] [--layers "1 2"] [--max-input-buffers N] [--no-gpu]
 
-# Default values
+set -euo pipefail
+
+# Default grid
 mtypes=("bert" "biobert" "pubmedbert" "bluebert" "scibert")
 layers=("1" "2" "3")
 
-# Parse command-line arguments
 INPUT_TEXT_TABLE_PATH=""
 OUTPUT_PARQUET_PREFIX=""
 KB_CSV_PATH=""
+MAX_INPUT_BUFFERS=""
+USE_GPU=1
+
+usage() {
+    echo "Usage: $0 --input-text-table-path PATH --output-parquet-path DIR --kb-csv-path PATH"
+    echo "          [--models \"m1 m2\"] [--layers \"1 2\"] [--max-input-buffers N] [--no-gpu]"
+}
 
 while [[ $# -gt 0 ]]; do
     case $1 in
@@ -23,47 +42,81 @@ while [[ $# -gt 0 ]]; do
             KB_CSV_PATH="$2"
             shift 2
             ;;
+        --models)
+            read -r -a mtypes <<< "${2//,/ }"
+            shift 2
+            ;;
+        --layers)
+            read -r -a layers <<< "${2//,/ }"
+            shift 2
+            ;;
+        --max-input-buffers)
+            MAX_INPUT_BUFFERS="$2"
+            shift 2
+            ;;
+        --no-gpu)
+            USE_GPU=0
+            shift
+            ;;
         *)
             echo "Unknown option: $1"
-            echo "Usage: $0 --input-text-table-path PATH --output-parquet-path PREFIX --kb-csv-path PATH"
+            usage
             exit 1
             ;;
     esac
 done
 
-# Validate required parameters
-if [[ -z "$INPUT_TEXT_TABLE_PATH" ]]; then
-    echo "Error: --input-text-table-path is required"
-    exit 1
-fi
-
-if [[ -z "$OUTPUT_PARQUET_PREFIX" ]]; then
-    echo "Error: --output-parquet-path is required"
-    exit 1
-fi
+for required in INPUT_TEXT_TABLE_PATH OUTPUT_PARQUET_PREFIX KB_CSV_PATH; do
+    if [[ -z "${!required}" ]]; then
+        echo "Error: missing required option for ${required}"
+        usage
+        exit 1
+    fi
+done
 
-if [[ -z "$KB_CSV_PATH" ]]; then
-    echo "Error: --kb-csv-path is required"
-    exit 1
+# The pairs KB carries is_symmetric; without it symmetric relations can be given an
+# inverse direction in the weak labels.
+if ! head -n 1 "$KB_CSV_PATH" | tr -d '\r' | tr ',' '\n' | grep -qx "is_symmetric"; then
+    echo "Warning: $KB_CSV_PATH has no is_symmetric column; pass the pairs KB" \
+         "(data/derived/properties.synthesis.2.pairs.csv) for symmetric handling."
 fi
 
-# Ensure output directory exists
 mkdir -p "$OUTPUT_PARQUET_PREFIX"
 
+# Refuse before doing any work, so a half-finished grid is never mixed with an old one.
+for model in "${mtypes[@]}"; do
+    for layer in "${layers[@]}"; do
+        output_file="${OUTPUT_PARQUET_PREFIX}/res_${model}_${layer}.parquet"
+        if [[ -e "$output_file" ]]; then
+            echo "Error: $output_file already exists; choose a new --output-parquet-path"
+            exit 1
+        fi
+    done
+done
+
+extra_args=()
+if [[ -n "$MAX_INPUT_BUFFERS" ]]; then
+    extra_args+=(--max-input-buffers "$MAX_INPUT_BUFFERS")
+fi
+if [[ "$USE_GPU" == "1" ]]; then
+    extra_args+=(--use-gpu)
+fi
+
 for model in "${mtypes[@]}"; do
     for layer in "${layers[@]}"; do
         output_file="${OUTPUT_PARQUET_PREFIX}/res_${model}_${layer}.parquet"
-        python run/embed_kb_corpus.py \
+        echo "== ${model} layer ${layer} -> ${output_file}"
+        uv run python run/embed_kb_corpus.py \
                --input-text-table-path "$INPUT_TEXT_TABLE_PATH" \
                --output-parquet-path "$output_file" \
                --kb-csv-path "$KB_CSV_PATH" \
                --model-type "$model" \
-               --nlp-model en_core_web_trf \
+               --nlp-model en_core_web_lg \
                --layers-spec "$layer" \
                --encoder-batch-size 100 \
                --input-buffer-rows 2000 \
                --negatives-per-positive 1.0 \
                --negative-seed 13 \
-               --use-gpu
+               ${extra_args[@]+"${extra_args[@]}"}
     done
 done
diff --git a/run/loop.fit.sh b/run/loop.fit.sh
index 4ddfc51..af3a280 100755
--- a/run/loop.fit.sh
+++ b/run/loop.fit.sh
@@ -108,7 +108,7 @@ for model in "${mtypes[@]}"; do
             report_path="$report_dir" \
             model_type="$model" \
             layers_spec="$layer_trimmed" \
-            nlp_model=en_core_web_trf \
+            nlp_model=en_core_web_lg \
             encoder_batch_size=100 \
             input_buffer_rows=2000 \
             use_gpu=true
diff --git a/run/obsolete/analysis/plot_discrim_dist.py b/run/obsolete/analysis/plot_discrim_dist.py
deleted file mode 100644
index c9ecaec..0000000
--- a/run/obsolete/analysis/plot_discrim_dist.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# pylint: disable=E1120
-
-import click
-import faiss
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import numpy as np
-
-from pelinker.util import load_models, encode
-from pelinker.preprocess import pre_process_properties
-from pelinker.util import fetch_latest_kb
-from pathlib import Path
-
-
-@click.command()
-@click.option(
-    "--model-type",
-    type=click.STRING,
-    default=["biobert"],
-    multiple=True,
-    help="run over BERT flavours",
-)
-def run(model_type):
-    model_type = sorted(model_type)
-    fig_path = "./figs"
-
-    path_derived = Path("./data/derived/")
-
-    fname, version = fetch_latest_kb(path_derived)
-
-    try:
-        df0 = pd.read_csv(path_derived / fname)
-    except Exception as e:
-        print(f"kb not found at {path_derived}")
-        raise e
-
-    report = pre_process_properties(df0)
-    labels = report.pop("labels")
-
-    layers = (
-        # [[-x] for x in range(1, 6)] +
-        # [[-x for x in range(1, 4)]] +
-        [[-1]]
-        +
-        # [[-1, -2, -8, -9]] +
-        ["sent"]
-    )
-
-    df_agg = []
-
-    for mt in model_type:
-        tokenizer, model = load_models(mt, False)
-        _, smodel = load_models(mt, True)
-        for ls in layers[:]:
-            tt_labels = encode(labels, tokenizer, smodel if ls == "sent" else model, ls)
-            layers_str = ls if ls == "sent" else "_".join([str(x) for x in ls])
-
-            index = faiss.IndexFlatIP(tt_labels.shape[1])
-            nb_nn = min([100, tt_labels.shape[0]])
-            index.add(tt_labels)
-
-            distance_matrix, nearest_neighbors_matrix = index.search(tt_labels, nb_nn)
-            ds = 1.0 - distance_matrix[:, 1]
-
-            dfa = pd.DataFrame(ds, columns=["delta"])
-            dfa["model_type"] = mt
-            dfa["layers"] = layers_str
-            df_agg += [dfa]
-    df0 = pd.concat(df_agg)
-    path = f"{fig_path}/discrim.pdf"
-    col_wrap = min([4, len(layers)])
-
-    sns.set_style("whitegrid")
-    g = sns.displot(
-        data=df0,
-        x="delta",
-        hue="model_type",
-        stat="density",
-        common_norm=False,
-        col="layers",
-        col_wrap=col_wrap,
-        bins=np.arange(0.0, 0.6, 0.05),
-        alpha=0.5,
-        facet_kws=dict(legend_out=False),
-    )
-    sns.move_legend(g, "upper right")
-    plt.savefig(path, bbox_inches="tight", dpi=300)
-
-
-if __name__ == "__main__":
-    run()
diff --git a/run/obsolete/analysis/plot_dispersion_hist.py b/run/obsolete/analysis/plot_dispersion_hist.py
deleted file mode 100644
index 296e11d..0000000
--- a/run/obsolete/analysis/plot_dispersion_hist.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# pylint: disable=E1120
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import numpy as np
-
-
-def run():
-    df0 = pd.read_csv("./data/derived/491_within_n_across_disps.csv")
-    df0 = df0.rename(columns={"within_verbs": "std"})
-    fig_path = "./figs"
-
-    sns.set_style("whitegrid")
-    _ = sns.displot(
-        data=df0,
-        x="std",
-        stat="density",
-        common_norm=False,
-        bins=np.arange(0.1, 0.7, 0.02),
-    )
-    path = f"{fig_path}/std.verb.pdf"
-    plt.savefig(path, bbox_inches="tight", dpi=300)
-
-
-if __name__ == "__main__":
-    run()
diff --git a/run/obsolete/analysis/plot_interterm_dist.py b/run/obsolete/analysis/plot_interterm_dist.py
deleted file mode 100644
index 8a0748e..0000000
--- a/run/obsolete/analysis/plot_interterm_dist.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# pylint: disable=E1120
-
-import click
-import faiss
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import numpy as np
-
-from pelinker.util import load_models, encode
-from pelinker.preprocess import pre_process_properties
-from pelinker.util import fetch_latest_kb
-from pathlib import Path
-
-
-@click.command()
-@click.option(
-    "--model-type",
-    type=click.STRING,
-    default=["biobert"],
-    multiple=True,
-    help="run over BERT flavours",
-)
-def run(model_type):
-    model_type = sorted(model_type)
-
-    fig_path = "./figs"
-
-    path_derived = Path("./data/derived/")
-
-    fname, version = fetch_latest_kb(path_derived)
-
-    try:
-        df0 = pd.read_csv(path_derived / fname)
-    except Exception as e:
-        print(f"kb not found at {path_derived}")
-        raise e
-
-    report = pre_process_properties(df0)
-    labels = report.pop("labels")
-
-    layers = (
-        # [[-x] for x in range(1, 6)] +
-        # [[-x for x in range(1, 4)]] +
-        [[-1], [-1, -2, -8, -9]] + ["sent"]
-    )
-
-    df_agg = []
-
-    for mt in model_type:
-        tokenizer, model = load_models(mt, False)
-        _, smodel = load_models(mt, True)
-        for ls in layers[:]:
-            tt_labels = encode(labels, tokenizer, smodel if ls == "sent" else model, ls)
-            layers_str = ls if ls == "sent" else "_".join([str(x) for x in ls])
-
-            index = faiss.IndexFlatIP(tt_labels.shape[1])
-            nb_nn = min([100, tt_labels.shape[0]])
-            index.add(tt_labels)
-
-            distance_matrix, nearest_neighbors_matrix = index.search(tt_labels, nb_nn)
-            ds = distance_matrix[:, 1:].flatten()
-            ds.sort()
-            k_links = 20
-            thr = ds[-k_links]
-
-            edges = []
-            for dd, nn in zip(distance_matrix, nearest_neighbors_matrix):
-                m = dd >= thr
-                equis = nn[m]
-                edges += [(equis[0], c) for c in equis[1:]]
-
-            dfa = pd.DataFrame(ds, columns=["dist"])
-            dfa["model_type"] = mt
-            dfa["layers"] = layers_str
-            df_agg += [dfa]
-    df0 = pd.concat(df_agg)
-    path = f"{fig_path}/interdist.new.pdf"
-    col_wrap = min([4, len(layers)])
-
-    sns.set_style("whitegrid")
-    _ = sns.displot(
-        data=df0,
-        x="dist",
-        hue="model_type",
-        stat="density",
-        common_norm=False,
-        col="layers",
-        col_wrap=col_wrap,
-        bins=np.arange(0.0, 1.1, 0.05),
-        alpha=0.5,
-    )
-    plt.savefig(path, bbox_inches="tight", dpi=300)
-
-
-if __name__ == "__main__":
-    run()
diff --git a/run/obsolete/csvcut.py b/run/obsolete/csvcut.py
deleted file mode 100644
index fe6f8d0..0000000
--- a/run/obsolete/csvcut.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import csv
-import sys
-
-reader = csv.reader(sys.stdin)
-writer = csv.writer(sys.stdout)
-
-for row in reader:
-    writer.writerow(row[1:])
diff --git a/run/obsolete/experiments/pattern_tensor_align.py b/run/obsolete/experiments/pattern_tensor_align.py
deleted file mode 100644
index 2b985cc..0000000
--- a/run/obsolete/experiments/pattern_tensor_align.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import click
-import pandas as pd
-import tqdm
-
-from torch.nn import CosineSimilarity
-from pelinker.onto import WordGrouping
-from pelinker.util import texts_to_vrep
-from pelinker.matching import match_pattern
-import pathlib
-import spacy
-import torch
-from pelinker.util import load_models, text_to_tokens
-from pelinker.model import Linker
-from pelinker.util import SimplifiedToken
-
-
-@click.command()
-@click.option(
-    "--model-type",
-    type=click.STRING,
-    default="biobert",
-    help="run over BERT flavours",
-)
-@click.option(
-    "--pattern",
-    type=click.STRING,
-    default=["dominate", "activate", "causes"],
-    multiple=True,
-    help="",
-)
-@click.option(
-    "--layers-spec",
-    type=click.STRING,
-    default="1",
-    help="`sent` or a string of layers, `1,2,3` would correspond to layers [-1, -2, -3]",
-)
-@click.option(
-    "--input-path",
-    type=click.Path(path_type=pathlib.Path),
-    default="./data/test/sample.csv.gz",
-    help="input df",
-)
-@click.option(
-    "--plot-path",
-    type=click.Path(path_type=pathlib.Path),
-    default="figs",
-    required=True,
-)
-def run(model_type, input_path, layers_spec, pattern, plot_path):
-    nlp = spacy.load("en_core_web_trf")
-
-    if not plot_path.exists():
-        plot_path.mkdir(parents=True, exist_ok=True)
-
-    tokenizer, model = load_models(model_type, sentence=False)
-    layers = Linker.str2layers(layers_spec)
-
-    df = pd.read_csv(input_path, index_col=0)
-    texts = df["abstract"]
-    indexes_of_interest_batch = []
-    for p in pattern:
-        indexes_of_interest_batch += [
-            [match_pattern(p, x, suffix_length=0) for x in texts]
-        ]
-
-    pattern_expressions: dict[str, list[SimplifiedToken]] = {
-        p: text_to_tokens(nlp=nlp, text=p) for p in pattern
-    }
-    pattern_lemmatized = [
-        " ".join([e.lemma for e in pe]) for pe in pattern_expressions.values()
-    ]
-
-    flags = [
-        any(True if ixs_ else False for ixs_ in item)
-        for item in zip(*indexes_of_interest_batch)
-    ]
-    data = [t for flag, t in zip(flags, texts) if flag]
-
-    batch_size = 40
-
-    data_batched = [data[i : i + batch_size] for i in range(0, len(data), batch_size)]
-
-    frep = []
-    tt_averages = []
-
-    for ibatch, text_batch in enumerate((pbar := tqdm.tqdm(data_batched))):
-        report_batch = texts_to_vrep(
-            text_batch,
-            tokenizer,
-            model,
-            layers,
-            [WordGrouping.W1],
-            nlp,
-        )
-
-        indexes_of_interest_batch = {
-            p: [match_pattern(p, text, suffix_length=0) for text in text_batch]
-            for p in pattern
-        }
-        # offsets = [report_batch.chunk_mapper.ichunk_char_offset(k) for k, _ in enumerate(report_batch.texts)]
-
-        for p in pattern:
-            # matches = indexes_of_interest_batch[p]
-            pe = pattern_expressions[p]
-            pe_lemmatized = " ".join([e.lemma for e in pe])
-            for w in report_batch.available_groupings():
-                expression_container = report_batch[w]
-                for itext, (text, expr_holder) in enumerate(
-                    zip(
-                        report_batch.texts,
-                        expression_container.expression_data,
-                    )
-                ):
-                    # if not matches:
-                    #     continue
-
-                    tt_averages += [expression_container.expression_data[0].tt]
-
-                    expr_lemma_match = expr_holder.filter_on_lemmas(pe)
-
-                    if not expr_lemma_match:
-                        continue
-                    offsets = [
-                        report_batch.chunk_mapper.map_chunk_to_text(e.itext, e.ichunk)
-                        for e, _ in expr_lemma_match
-                    ]
-
-                    frep += [
-                        (
-                            {
-                                "word_grouping": w,
-                                "idoc": ibatch * batch_size + itext,
-                                "pattern_lemmatized": pe_lemmatized,
-                                "mention": text[offset + e.a : offset + e.b],
-                                "mention_": " ".join([t.text for t in e.tokens]),
-                                "mention_lemmatized": " ".join(
-                                    [t.lemma for t in e.tokens]
-                                ),
-                                "tensor": tt,
-                            }
-                        )
-                        for (e, tt), offset in zip(expr_lemma_match, offsets)
-                    ]
-
-        pbar.set_description(f"entities added : {len(frep)}")
-
-    tt_all = (torch.stack([x["tensor"] for x in frep]), "all.patterns")
-    tt_pats = [
-        (torch.stack([x["tensor"] for x in frep if x["pattern_lemmatized"] == p]), p)
-        for p in pattern_lemmatized
-    ]
-
-    tt_means = (torch.cat(tt_averages), "all")
-    cos = CosineSimilarity(dim=1, eps=1e-6)
-
-    tts_cmp = tt_pats + [tt_all, tt_means]
-
-    vc_mentions_combination = (
-        pd.DataFrame(
-            [{k: v for k, v in item.items() if k != "tensor"} for item in frep],
-        )
-        .apply(lambda x: ": ".join(x[["pattern_lemmatized", "mention_"]]), axis=1)
-        .value_counts()
-    )
-
-    print(vc_mentions_combination)
-
-    cos_dist = []
-    for t, label in tts_cmp:
-        if label == "all.patterns":
-            df_labels = pd.DataFrame(
-                [x["pattern_lemmatized"] for x in frep], columns=["label"]
-            )
-            vc_labels = (
-                df_labels.groupby("label")
-                .apply(lambda x: x.shape[0])
-                .reset_index()
-                .rename(columns={0: "cnt"})
-            )
-            vc_labels["weight"] = 1 / vc_labels["cnt"]
-            df_labels = df_labels.merge(vc_labels, on="label")
-            tt_weight = torch.from_numpy(df_labels["weight"].values)
-        else:
-            tt_weight = torch.ones(t.shape[:1])
-        t_center = (tt_weight.unsqueeze(-1) * t).mean(0).unsqueeze(0)
-        t_center_normed = (
-            ((tt_weight.unsqueeze(-1) * t) / t.norm(dim=-1).unsqueeze(-1))
-            .mean(0)
-            .unsqueeze(0)
-        )
-        dists = cos(t, t_center).tolist()
-        cos_dist += [(d, f"{label}") for d in dists]
-        dists = cos(t, t_center_normed).tolist()
-        cos_dist += [(d, f"{label}.normed") for d in dists]
-
-    df = pd.DataFrame(cos_dist, columns=["d", "label"])
-    mean_dist = df.groupby("label").apply(lambda x: x["d"].mean())
-
-    print(df["label"].value_counts())
-    print(mean_dist)
-
-    try:
-        import seaborn as sns
-        import matplotlib.pyplot as plt
-
-        sns.set_style("whitegrid")
-        _ = sns.displot(
-            df.loc[~df["label"].apply(lambda x: x.endswith(".normed"))],
-            x="d",
-            hue="label",
-            kind="kde",
-            common_norm=False,
-            # stat="density",
-            # element="step",
-            # bins=20
-        )
-
-        plt.savefig(
-            plot_path.expanduser() / f"cos_sim_dist.{model_type}.{layers_spec}.pdf",
-            bbox_inches="tight",
-        )
-        plt.close()
-    except Exception as e:
-        print(f"something happened : {e}")
-
-
-if __name__ == "__main__":
-    run()
diff --git a/run/obsolete/preprocessing/disambiguate.py b/run/obsolete/preprocessing/disambiguate.py
deleted file mode 100644
index 6381618..0000000
--- a/run/obsolete/preprocessing/disambiguate.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# pylint: disable=E1120
-
-import click
-import faiss
-import pandas as pd
-import pathlib
-from pelinker.util import load_models, encode
-from pelinker.preprocess import pre_process_properties
-from pelinker.util import fetch_latest_kb
-from pathlib import Path
-
-
-@click.command()
-@click.option(
-    "--rep-string",
-    type=click.STRING,
-    default="1,2,3",
-    help="`sent` or a string of layers, `1,2,3` would correspond to layers [-1, -2, -3]",
-)
-@click.option(
-    "--model-type",
-    type=click.STRING,
-    default="biobert",
-    help="choose BERT flavours",
-)
-@click.option(
-    "--kb-path",
-    type=click.Path(path_type=pathlib.Path),
-    default="data/derived",
-    help="choose BERT flavours",
-)
-def run(rep_string, model_type, kb_path):
-    path_derived = Path(kb_path)
-
-    fname, version = fetch_latest_kb(path_derived)
-
-    try:
-        df0 = pd.read_csv(path_derived / fname)
-    except Exception as e:
-        print(f"kb not found at {path_derived}")
-        raise e
-
-    report = pre_process_properties(df0)
-    labels = report.pop("labels")
-    if rep_string != "sent":
-        try:
-            layers = rep_string.split(",")
-            ls = set([-int(x) for x in layers])
-        except:
-            raise ValueError(f"{rep_string} could not be parsed into layers")
-        sentence = False
-    else:
-        sentence = True
-        ls = rep_string
-
-    # labels = labels[:] + ["queen", "king", "chess", "US Embassy", "Bill Gates"]
-    tokenizer, model = load_models(model_type, sentence)
-    tt_labels = encode(labels, tokenizer, model, ls)
-
-    index = faiss.IndexFlatIP(tt_labels.shape[1])
-    nb_nn = min([100, tt_labels.shape[0]])
-    index.add(tt_labels)
-
-    distance_matrix, nearest_neighbors_matrix = index.search(tt_labels, nb_nn)
-    ds = distance_matrix[:, 1:].flatten()
-    ds.sort()
-    k_links = 20
-
-    print(ds[-k_links:])
-    thr = ds[-k_links]
-
-    edges = []
-    for dd, nn in zip(distance_matrix, nearest_neighbors_matrix):
-        m = dd >= thr
-        equis = nn[m]
-        edges += [tuple(sorted((equis[0], c))) for c in equis[1:]]
-
-    edges = set(edges)
-    nlabels = [(labels[a], labels[b]) for a, b in edges]
-    print(nlabels)
-
-
-if __name__ == "__main__":
-    run()
diff --git a/run/obsolete/test.pat.align.sh b/run/obsolete/test.pat.align.sh
deleted file mode 100755
index 4ffec8f..0000000
--- a/run/obsolete/test.pat.align.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-
-mtypes=("bert" "scibert" "biobert" "pubmedbert" "biobert-stsb")
-
-cd .. || exit
-
-for model in "${mtypes[@]}"; do
-  poetry run python -m run.experiments.pattern_tensor_align \
-    --model-type "$model" \
-    "$@"
-done
diff --git a/run/obsolete/testing/run_pel_description.py b/run/obsolete/testing/run_pel_description.py
deleted file mode 100644
index 7ec606a..0000000
--- a/run/obsolete/testing/run_pel_description.py
+++ /dev/null
@@ -1,158 +0,0 @@
-# pylint: disable=E1120
-
-import click
-import faiss
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import torch
-
-import seaborn as sns
-
-from pelinker.util import (
-    text_to_tokens_embeddings,
-    tt_aggregate_normalize,
-    load_models,
-    compute_distance_ref,
-)
-from pelinker.preprocess import pre_process_properties
-from pelinker.util import fetch_latest_kb
-from pathlib import Path
-
-
-@click.command()
-@click.option(
-    "--model-type",
-    type=click.STRING,
-    default=["scibert"],
-    multiple=True,
-    help="run over BERT flavours",
-)
-@click.option(
-    "--superposition",
-    type=click.BOOL,
-    default=False,
-    is_flag=True,
-    help="use a superposition of label and description embeddings, where available",
-)
-def run(model_type, superposition):
-    fig_path = "./figs"
-    report_path = "./reports"
-
-    suffix = "_superposition" if superposition else ""
-
-    path_derived = Path("./data/derived/")
-
-    fname, version = fetch_latest_kb(path_derived)
-
-    try:
-        df0 = pd.read_csv(path_derived / fname)
-    except Exception as e:
-        print(f"kb not found at {path_derived}")
-        raise e
-
-    report = pre_process_properties(df0)
-    labels = report.pop("labels")
-    descriptions = report.pop("descriptions")
-    ixlabel_ixdesc = report.pop("ixlabel_ixdesc")
-    property_from_desc = report.pop("property_from_desc")
-    id_ixlabel_map = report.pop("id_ixlabel_map")
-
-    layers = (
-        [[-x] for x in range(1, 10)]
-        + [[-x for x in range(1, 4)]]
-        + [[-x for x in range(1, 8)]]
-        + [[-1, -2, -8, -9]]
-    )
-
-    metrics = []
-    df_agg = []
-
-    for mt in model_type:
-        tokenizer, model = load_models(mt)
-
-        tt_labels_layered, labels_spans = text_to_tokens_embeddings(
-            labels, tokenizer, model
-        )
-
-        tt_descs_layered, desc_spans = text_to_tokens_embeddings(
-            descriptions,
-            tokenizer,
-            model,
-        )
-
-        for ls in layers:
-            tt_labels = tt_aggregate_normalize(tt_labels_layered, ls)
-            tt_descs = tt_aggregate_normalize(tt_descs_layered, ls)
-
-            if superposition:
-                tt_basis = []
-                for j, tt in enumerate(tt_labels):
-                    if j in ixlabel_ixdesc:
-                        tt_basis += [tt + tt_descs[ixlabel_ixdesc[j]]]
-                    else:
-                        tt_basis += [tt]
-
-                tt_basis = torch.stack(tt_basis)
-                tt_basis = tt_basis / tt_basis.norm(dim=1).unsqueeze(1)
-            else:
-                tt_basis = tt_labels
-
-            index = faiss.IndexFlatIP(tt_basis.shape[1])
-            index.add(tt_basis)
-
-            nb_nn = min([10, tt_basis.shape[0]])
-            layers_str = "_".join([str(x) for x in ls])
-
-            m0, dfa = compute_distance_ref(
-                index,
-                tt_descs,
-                nb_nn,
-                np.array([id_ixlabel_map[p] for p in property_from_desc]),
-            )
-            dfa["model_type"] = mt
-            dfa["layers"] = layers_str
-
-            df_agg += [dfa]
-            metrics += [(mt, layers_str, *m0)]
-
-    df0 = pd.concat(df_agg)
-    _ = sns.displot(
-        data=df0,
-        x="dist",
-        hue="position",
-        stat="density",
-        common_norm=False,
-        col="layers",
-        row="model_type",
-        bins=np.arange(0.2, 1.1, 0.05),
-        aspect=0.7,
-    )
-
-    path = f"{fig_path}/desc2label_dist{suffix}.png"
-    plt.savefig(path, bbox_inches="tight", dpi=300)
-    plt.close()
-    metrics_df = pd.DataFrame(metrics, columns=["model_type", "layers", "dist", "acc"])
-    metrics_df.to_csv(f"{report_path}/metrics_desc2label{suffix}.csv")
-
-    min_len = min(metrics_df["layers"].apply(lambda x: len(x)))
-    metrics_df2 = metrics_df[
-        metrics_df["layers"].apply(lambda x: len(x) == min_len)
-    ].copy()
-    metrics_df2["layers"] = metrics_df2["layers"].astype(int)
-
-    sns.lineplot(metrics_df2, hue="model_type", x="layers", y="acc")
-    path = f"{fig_path}/desc2label_accuracy{suffix}.png"
-    plt.savefig(path, bbox_inches="tight", dpi=300)
-    plt.close()
-
-    sns.lineplot(metrics_df2, hue="model_type", x="layers", y="dist")
-    path = f"{fig_path}/desc2label_dist_modes{suffix}.png"
-    plt.savefig(path, bbox_inches="tight", dpi=300)
-    plt.close()
-
-    print(metrics_df)
-
-
-if __name__ == "__main__":
-    run()
diff --git a/run/obsolete/testing/test_linking.py b/run/obsolete/testing/test_linking.py
deleted file mode 100644
index 3053423..0000000
--- a/run/obsolete/testing/test_linking.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# pylint: disable=E1120
-
-import logging
-
-import faiss
-import pandas as pd
-import torch
-from transformers import AutoModel, AutoTokenizer
-from pelinker.util import fetch_latest_kb
-from pathlib import Path
-
-
-def vectorize_text(text, tokenizer, model):
-    inputs = tokenizer(
-        text, return_tensors="pt", padding=True, truncation=True, max_length=512
-    )
-
-    inputs = {k: v.to(model.device) for k, v in inputs.items()}
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    return outputs.last_hidden_state.mean(dim=1).squeeze().cpu()
-
-
-def main():
-    path_derived = Path("./data/derived/")
-
-    fname, version = fetch_latest_kb(path_derived)
-
-    try:
-        df = pd.read_csv(path_derived / fname)
-    except Exception as e:
-        print(f"kb not found at {path_derived}")
-        raise e
-
-    transformers_logger = logging.getLogger("transformers")
-    transformers_logger.setLevel(logging.WARNING)
-
-    tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-base-cased-v1.2")
-    model = AutoModel.from_pretrained("dmis-lab/biobert-base-cased-v1.2")
-
-    labels = [
-        (j, p, vectorize_text(x, tokenizer, model))
-        for j, (p, x) in enumerate(df[["entity_ids", "label"]].values)
-    ]
-    descs = [
-        (j, p, vectorize_text(x, tokenizer, model))
-        for j, (p, x) in enumerate(
-            df.loc[df["description"].notnull(), ["entity_ids", "description"]].values
-        )
-    ]
-
-    tt_labels = torch.stack([x[-1] for x in labels])
-    tt_descs = torch.stack([x[-1] for x in descs])
-
-    tt_labels = tt_labels / tt_labels.norm(dim=1).unsqueeze(1)
-    tt_descs = tt_descs / tt_descs.norm(dim=1).unsqueeze(1)
-
-    index = faiss.IndexFlatIP(tt_labels.shape[1])
-    nb_nn = 3
-    index.add(tt_labels)
-    distance_matrix, nearest_neighbors_matrix = index.search(tt_descs, nb_nn)
-    nb_nn = 10
-
-
-if __name__ == "__main__":
-    main()
diff --git a/run/obsolete/testing/test_server.py b/run/obsolete/testing/test_server.py
deleted file mode 100644
index fecaf9a..0000000
--- a/run/obsolete/testing/test_server.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from pprint import pprint
-import requests
-import click
-
-
-@click.command()
-@click.option("--port", type=click.INT, default=8599)
-@click.option("--host", type=click.STRING, default="localhost")
-def run(host, port):
-    text = (
-        "TAMs can also secrete in the TME a number of immunosuppressive cytokines, "
-        "such as IL-6, TGF-β, and IL-10 that are able to suppress CD8+ T-cell function."
-    )
-    text = "Objectives"
-
-    url = f"http://{host}:{port}/link"
-    r = requests.post(url, json={"text": text}, verify=False)
-    jr = r.json()
-    pprint(jr)
-
-
-if __name__ == "__main__":
-    run()
diff --git a/run/preprocessing/derive_inverse_pairs.py b/run/preprocessing/derive_inverse_pairs.py
new file mode 100644
index 0000000..3e6728f
--- /dev/null
+++ b/run/preprocessing/derive_inverse_pairs.py
@@ -0,0 +1,440 @@
+"""Derive direct/inverse label pairs for the whole KB, not just RO's declared ones.
+
+`owl:inverseOf` covers a minority of the vocabulary, but the KB carries both members of
+many more converse pairs as separate entries ("regulates" / "regulated by", "has part" /
+"part of"). Directionality work needs the *whole* mapping: stage (A) has to send a passive
+mention to the converse entry, and the evaluation has to know which pairs exist before it
+can report accuracy by direction.
+
+Pairs come from four rules, each recorded in ``inverse_source`` so nothing derived is
+mistaken for something asserted:
+
+- ``ro`` — declared ``owl:inverseOf``. Authoritative; never overridden.
+- ``has_of`` — the OBO surface pattern ``has X`` ↔ ``X of`` ("has part" / "part of").
+- ``passive_by`` — ``X-ed by`` ↔ the active entry with the same verb lemma
+  ("regulated by" / "regulates").
+- ``passive_prep`` — ``X-ed <prep>`` ↔ the same active lemma, for the remaining
+  prepositions ("expressed in" / "expresses").
+
+Only the first is a fact about the ontology. The other three are **candidates for human
+review**: they are proposals a curator accepts or rejects, and the output marks them
+``needs_review`` so an unreviewed guess can never silently become ground truth.
+
+**Symmetric relations have no converse.** "interacts with", "overlaps", "connected to"
+read the same from either end, so a reversed or passive mention is still the same relation
+with ``direction=symmetric``. They are flagged ``is_symmetric`` — from RO (see
+``extract_properties_ro.py``) plus a curated list for entries RO does not cover
+(``--symmetric-csv``, e.g. "binds to", "associated with") — and the surface rules never
+pair them: a symmetric label that
+merely looks passive ("connected to") would otherwise be folded onto an unrelated
+look-alike ("connects").
+
+Each pair also gets a **canonical member**. This matters for annotation: while the KB
+holds both members, a passive mention has two equally valid encodings — ("regulated by",
+forward) or ("regulates", inverse) — and letting an annotator choose either is exactly the
+ambiguity that put contradictory labels on the same span in the weak supervision. Gold
+therefore offers only canonical labels and carries the orientation in ``direction``.
+Canonical is the non-converse surface form (not ``X-ed by``/``X of``); ties break on the
+smaller entity id so the choice is stable.
+
+Output: ``data/derived/properties.synthesis.2.pairs.csv`` — the KB plus
+``inverse_entity_id`` / ``inverse_label`` / ``inverse_source`` / ``needs_review`` /
+``is_symmetric`` / ``is_canonical`` / ``canonical_entity_id``. This is the KB the gold
+pipeline consumes; the ``*.inverse.csv`` read here is its *input*, and has none of those
+columns.
+
+**The review loop.** Every unreviewed derived pair is written to
+``<output>.pending_review.csv``. A curator fills in ``verdict`` (``accept`` or ``reject``)
+and passes the file back as ``--review-csv``: an accepted pair loses ``needs_review``, and
+a rejected one is unlinked outright, so each member goes back to being its own canonical
+form. Rejection matters more than it looks — an unreviewed wrong pair collapses two
+distinct relations onto one canonical id, and prompts, gold and scores then agree with the
+mistake instead of exposing it.
+
+Usage:
+
+    uv run python run/preprocessing/derive_inverse_pairs.py \
+        --kb-csv-path data/derived/properties.synthesis.2.inverse.csv \
+        --output-path data/derived/properties.synthesis.2.pairs.csv \
+        --review-csv data/curated/inverse_pairs.review.csv \
+        --ro-csv-path data/derived/properties.ro.csv \
+        --symmetric-csv data/curated/symmetric.csv
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+import click
+import pandas as pd
+import spacy
+from pelinker.core.paths import ExpandedPath
+from pelinker.text.predicates import PREPOSITIONS as _PREPOSITIONS
+from pelinker.text.predicates import is_passive_form
+
+logger = logging.getLogger(__name__)
+
+
+def verb_lemma_key(label: str, nlp) -> str | None:
+    """Lemma of a label's leading verb phrase, ignoring a trailing preposition.
+
+    ``regulated by`` and ``regulates`` both key on ``regulate``, which is what makes the
+    two entries recognisable as one relation seen from two ends.
+    """
+    doc = nlp(label)
+    tokens = [t for t in doc if not (t.is_punct or t.is_space)]
+    if not tokens:
+        return None
+    if tokens[-1].text.lower() in _PREPOSITIONS:
+        tokens = tokens[:-1]
+    if not tokens:
+        return None
+    # Drop a leading copula ("is expressed in" → "expressed in").
+    if tokens[0].lemma_.lower() in {"be", "is"} and len(tokens) > 1:
+        tokens = tokens[1:]
+    return " ".join(t.lemma_.lower() for t in tokens)
+
+
+def attach_symmetry(
+    kb: pd.DataFrame,
+    ro: pd.DataFrame | None,
+    curated: frozenset[str] = frozenset(),
+) -> pd.DataFrame:
+    """Ensure ``is_symmetric``: the KB's own flag, else RO's, plus the ``curated`` ids.
+
+    The curated KB is hand-selected from RO and does not carry RO's axioms, so symmetry is
+    joined back on ``entity_id``. Entries RO does not know (GeneWays, PEL) are symmetric
+    only when a curator lists them.
+    """
+    work = kb.copy()
+    ids = work["entity_id"].astype(str)
+    if "is_symmetric" in work.columns:
+        flag = work["is_symmetric"].fillna(False).astype(bool)
+    elif ro is not None and "is_symmetric" in ro.columns:
+        from_ro = set(ro.loc[ro["is_symmetric"].astype(bool), "entity_id"].astype(str))
+        flag = ids.isin(from_ro)
+    else:
+        flag = pd.Series(False, index=work.index)
+    work["is_symmetric"] = flag | ids.isin(curated)
+    return work
+
+
+def load_symmetric(path: str | Path) -> frozenset[str]:
+    """Curated symmetric entity ids (``entity_id`` column; ``label``/``note`` are for people)."""
+    sheet = pd.read_csv(path, dtype=str).fillna("")
+    if "entity_id" not in sheet.columns:
+        raise click.ClickException(f"{path}: symmetric sheet needs an entity_id column")
+    return frozenset(e.strip() for e in sheet["entity_id"] if e.strip())
+
+
+def derive_pairs(kb: pd.DataFrame, nlp) -> pd.DataFrame:
+    """Attach an inverse to every entry we can, recording how it was obtained."""
+    work = kb.copy()
+    work["label"] = work["label"].astype(str)
+    work["entity_id"] = work["entity_id"].astype(str)
+    if "is_symmetric" not in work.columns:
+        work["is_symmetric"] = False
+    symmetric = set(work.loc[work["is_symmetric"].astype(bool), "entity_id"])
+
+    by_label = dict(zip(work["label"], work["entity_id"]))
+    label_of = {v: k for k, v in by_label.items()}
+    known_ids = set(work["entity_id"])
+
+    inverse_id: dict[str, str] = {}
+    source: dict[str, str] = {}
+
+    def link(a_id: str, b_id: str, rule: str) -> None:
+        """Record a pair both ways, never overwriting a stronger rule.
+
+        A symmetric relation is its own converse, so a surface rule that pairs it with
+        another entry is always wrong.
+        """
+        if a_id in symmetric or b_id in symmetric:
+            return
+        for x, y in ((a_id, b_id), (b_id, a_id)):
+            if source.get(x) == "ro":
+                continue
+            if x in inverse_id and source.get(x) != rule:
+                continue
+            inverse_id[x] = y
+            source[x] = rule
+
+    # 1. Declared inverses win outright.
+    if "inverse_entity_id" in work.columns:
+        for _, row in work.iterrows():
+            other = row["inverse_entity_id"]
+            if isinstance(other, str) and other in known_ids:
+                inverse_id[row["entity_id"]] = other
+                source[row["entity_id"]] = "ro"
+
+    # 2. "has X" <-> "X of".
+    for label, eid in by_label.items():
+        low = label.lower()
+        if low.startswith("has "):
+            stem = low[4:].strip()
+            partner = by_label.get(f"{stem} of")
+            if partner is not None:
+                link(eid, partner, "has_of")
+
+    # 3/4. Passive surface forms against the active entry with the same verb lemma.
+    lemma_to_active: dict[str, list[str]] = {}
+    for label, eid in by_label.items():
+        if is_passive_form(label):
+            continue
+        key = verb_lemma_key(label, nlp)
+        if key:
+            lemma_to_active.setdefault(key, []).append(eid)
+
+    for label, eid in by_label.items():
+        if not is_passive_form(label):
+            continue
+        key = verb_lemma_key(label, nlp)
+        if not key:
+            continue
+        candidates = [c for c in lemma_to_active.get(key, []) if c != eid]
+        if len(candidates) != 1:
+            # Ambiguous or absent: leave it for a curator rather than guessing.
+            continue
+        rule = "passive_by" if label.lower().endswith(" by") else "passive_prep"
+        link(eid, candidates[0], rule)
+
+    work["inverse_entity_id"] = work["entity_id"].map(inverse_id)
+    work["inverse_label"] = work["inverse_entity_id"].map(label_of)
+    work["inverse_source"] = work["entity_id"].map(source)
+    work["needs_review"] = work["inverse_source"].isin(
+        ["has_of", "passive_by", "passive_prep"]
+    )
+
+    def converse_surface(label: str) -> bool:
+        low = label.lower()
+        return is_passive_form(label) or low.endswith(" of")
+
+    canonical: dict[str, str] = {}
+    for _, row in work.iterrows():
+        eid, label = row["entity_id"], row["label"]
+        other = inverse_id.get(eid)
+        if other is None:
+            canonical[eid] = eid  # unpaired entries are their own canonical form
+            continue
+        other_label = label_of[other]
+        self_converse, other_converse = (
+            converse_surface(label),
+            converse_surface(other_label),
+        )
+        if self_converse != other_converse:
+            canonical[eid] = other if self_converse else eid
+        else:
+            canonical[eid] = min(eid, other)
+
+    work["canonical_entity_id"] = work["entity_id"].map(canonical)
+    work["is_canonical"] = work["canonical_entity_id"] == work["entity_id"]
+    return work
+
+
+_VERDICTS = ("accept", "reject")
+
+REVIEW_COLUMNS = ("entity_id", "inverse_entity_id", "verdict", "note")
+
+
+def load_review(path: str | Path) -> dict[frozenset[str], str]:
+    """Curator verdicts, keyed on the unordered pair so one row decides both members."""
+    review = pd.read_csv(path, dtype=str).fillna("")
+    missing = [c for c in REVIEW_COLUMNS if c not in review.columns]
+    if missing:
+        raise click.ClickException(
+            f"{path}: review sheet is missing column(s) {', '.join(missing)}"
+        )
+    verdicts: dict[frozenset[str], str] = {}
+    for _, row in review.iterrows():
+        verdict = row["verdict"].strip().lower()
+        if not verdict:
+            continue
+        if verdict not in _VERDICTS:
+            raise click.ClickException(
+                f"{path}: verdict for {row['entity_id']}/{row['inverse_entity_id']} must "
+                f"be one of {list(_VERDICTS)}, got {row['verdict']!r}"
+            )
+        verdicts[
+            frozenset({row["entity_id"].strip(), row["inverse_entity_id"].strip()})
+        ] = verdict
+    return verdicts
+
+
+def apply_review(
+    paired: pd.DataFrame, verdicts: dict[frozenset[str], str]
+) -> pd.DataFrame:
+    """Accepted pairs stop needing review; rejected pairs are unlinked entirely.
+
+    A rejected pair is not merely "unreviewed": the curator has said these two entries are
+    *not* converses, so leaving the link in place would keep collapsing them onto one
+    canonical id. Each member becomes unpaired and its own canonical form.
+    """
+    if not verdicts:
+        return paired
+    work = paired.copy()
+    rejected: set[str] = set()
+    for _, row in work.iterrows():
+        other = row["inverse_entity_id"]
+        if not isinstance(other, str) or not other:
+            continue
+        verdict = verdicts.get(frozenset({str(row["entity_id"]), other}))
+        if verdict == "reject":
+            rejected.add(str(row["entity_id"]))
+        elif verdict == "accept":
+            work.loc[work["entity_id"] == row["entity_id"], "needs_review"] = False
+
+    if rejected:
+        mask = work["entity_id"].isin(rejected)
+        work.loc[mask, ["inverse_entity_id", "inverse_label", "inverse_source"]] = None
+        work.loc[mask, "needs_review"] = False
+        work.loc[mask, "canonical_entity_id"] = work.loc[mask, "entity_id"]
+        work["is_canonical"] = work["canonical_entity_id"] == work["entity_id"]
+    return work
+
+
+def pending_review_sheet(paired: pd.DataFrame) -> pd.DataFrame:
+    """One row per unreviewed heuristic pair, with the wording a curator has to judge."""
+    pending = paired.loc[paired["needs_review"].astype(bool)]
+    rows = []
+    seen: set[frozenset[str]] = set()
+    for _, row in pending.iterrows():
+        key = frozenset({str(row["entity_id"]), str(row["inverse_entity_id"])})
+        if key in seen:
+            continue
+        seen.add(key)
+        rows.append(
+            {
+                "entity_id": row["entity_id"],
+                "label": row["label"],
+                "inverse_entity_id": row["inverse_entity_id"],
+                "inverse_label": row["inverse_label"],
+                "inverse_source": row["inverse_source"],
+                "canonical_entity_id": row["canonical_entity_id"],
+                "description": row.get("description", ""),
+                "verdict": "",
+                "note": "",
+            }
+        )
+    return pd.DataFrame(rows, columns=list(rows[0]) if rows else list(REVIEW_COLUMNS))
+
+
+@click.command()
+@click.option(
+    "--kb-csv-path",
+    default="data/derived/properties.synthesis.2.inverse.csv",
+    show_default=True,
+    type=ExpandedPath(exists=True),
+)
+@click.option(
+    "--output-path",
+    default="data/derived/properties.synthesis.2.pairs.csv",
+    show_default=True,
+    type=ExpandedPath(),
+)
+@click.option(
+    "--review-csv",
+    default="data/curated/inverse_pairs.review.csv",
+    show_default=True,
+    help=(
+        "Curator verdicts (entity_id, inverse_entity_id, verdict=accept|reject, note). "
+        "Missing file is fine on a first run."
+    ),
+)
+@click.option(
+    "--ro-csv-path",
+    default="data/derived/properties.ro.csv",
+    show_default=True,
+    help=(
+        "RO extraction carrying `is_symmetric`; used when the KB lacks the column. "
+        "Missing file means no entry is treated as symmetric."
+    ),
+)
+@click.option(
+    "--symmetric-csv",
+    default="data/curated/symmetric.csv",
+    show_default=True,
+    help="Curated symmetric entity ids, for entries RO does not declare. Optional.",
+)
+@click.option("--nlp-model", default="en_core_web_lg", show_default=True)
+def main(
+    kb_csv_path: str,
+    output_path: str,
+    review_csv: str,
+    ro_csv_path: str,
+    symmetric_csv: str,
+    nlp_model: str,
+) -> None:
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    kb = pd.read_csv(kb_csv_path)
+    ro_path = Path(ro_csv_path).expanduser()
+    ro = pd.read_csv(ro_path) if ro_path.exists() else None
+    if ro is None:
+        logger.warning("No RO extraction at %s — RO symmetry is not applied", ro_path)
+    sym_path = Path(symmetric_csv).expanduser()
+    curated = load_symmetric(sym_path) if sym_path.exists() else frozenset()
+    unknown = curated - set(kb["entity_id"].astype(str))
+    if unknown:
+        raise click.ClickException(
+            f"{sym_path}: ids not in the KB: {', '.join(sorted(unknown))}"
+        )
+    kb = attach_symmetry(kb, ro, curated)
+    nlp = spacy.load(nlp_model, exclude=["parser", "ner"])
+
+    paired = derive_pairs(kb, nlp)
+
+    review_path = Path(review_csv).expanduser()
+    if review_path.exists():
+        verdicts = load_review(review_path)
+        paired = apply_review(paired, verdicts)
+        logger.info("Applied %d curator verdicts from %s", len(verdicts), review_path)
+    else:
+        logger.info(
+            "No review sheet at %s — every derived pair stays unreviewed", review_path
+        )
+
+    pending = pending_review_sheet(paired)
+    pending_path = Path(output_path).with_suffix(".pending_review.csv")
+    if len(pending):
+        pending.to_csv(pending_path, index=False)
+        logger.warning(
+            "%d derived pairs await a curator verdict → %s. Fill in `verdict` "
+            "(accept|reject) and pass the file as --review-csv; until then a wrong pair "
+            "silently merges two relations onto one canonical id.",
+            len(pending),
+            pending_path,
+        )
+    elif pending_path.exists():
+        pending_path.unlink()
+
+    counts = paired["inverse_source"].value_counts(dropna=False).to_dict()
+    logger.info("Entries: %d", len(paired))
+    logger.info("With an inverse: %d", int(paired["inverse_entity_id"].notna().sum()))
+    logger.info("By rule: %s", counts)
+    logger.info("Needing review: %d", int(paired["needs_review"].sum()))
+    logger.info("Symmetric: %d", int(paired["is_symmetric"].sum()))
+    logger.info(
+        "Canonical labels offered to annotation: %d of %d",
+        int(paired["is_canonical"].sum()),
+        len(paired),
+    )
+
+    cols = [
+        "entity_id",
+        "label",
+        "description",
+        "inverse_entity_id",
+        "inverse_label",
+        "inverse_source",
+        "needs_review",
+        "is_symmetric",
+        "is_canonical",
+        "canonical_entity_id",
+        "example",
+    ]
+    paired[[c for c in cols if c in paired.columns]].to_csv(output_path, index=False)
+    logger.info("Wrote %s", output_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run/preprocessing/extract_properties_ro.py b/run/preprocessing/extract_properties_ro.py
index 72f3541..c2955cc 100644
--- a/run/preprocessing/extract_properties_ro.py
+++ b/run/preprocessing/extract_properties_ro.py
@@ -1,7 +1,44 @@
 import pandas as pd
 import rdflib
 from rdflib import Namespace
-from rdflib.namespace import OWL, RDFS
+from rdflib.collection import Collection
+from rdflib.namespace import OWL, RDF, RDFS
+
+
+def iri_to_entity_id(iri: str) -> str:
+    """``.../RO_0002206`` → ``RO.0002206`` (the id form used across the KB)."""
+    return ".".join(str(iri).split("/")[-1].split("_"))
+
+
+def symmetric_property_iris(g: rdflib.Graph) -> set[rdflib.URIRef]:
+    """Properties whose orientation carries no information.
+
+    Two ways a property is symmetric in RO:
+
+    - declared ``owl:SymmetricProperty`` ("interacts with", "overlaps");
+    - defined by the chain ``inverse(P) ∘ P`` — "a connected to b iff some c connects a
+      and c connects b". The chain reads the same from either end, so the property is
+      symmetric by construction even where RO omits the declaration.
+
+    Symmetry matters downstream because a symmetric relation has no converse entry: a
+    passive or reversed mention is still the same relation, and pairing it with a
+    look-alike label ("connected to" / "connects") would fold two relations onto one id.
+    """
+    found = {
+        s
+        for s in g.subjects(RDF.type, OWL.SymmetricProperty)
+        if isinstance(s, rdflib.URIRef)
+    }
+    for prop, chain in g.subject_objects(OWL.propertyChainAxiom):
+        if not isinstance(prop, rdflib.URIRef):
+            continue
+        links = list(Collection(g, chain))
+        if len(links) != 2:
+            continue
+        first, second = links
+        if g.value(first, OWL.inverseOf) == second:
+            found.add(prop)
+    return found
 
 
 def main():
@@ -40,8 +77,18 @@ def main():
 
     descs = [x for x in r]
 
-    ids = [x.split("/")[-1] for x in properties]
-    ids = [".".join(x.split("_")) for x in ids]
+    # Inverse pairs are the KB's own statement of predicate orientation: "activates" /
+    # "activated by" are one relation read from two ends. They were dropped here before,
+    # which left the pipeline with no way to tell a predicate from its converse.
+    query = """
+    SELECT ?subject ?inverse
+    WHERE {
+        ?subject owl:inverseOf ?inverse .
+    }
+    """
+    inverses = [x for x in g.query(query)]
+
+    ids = [iri_to_entity_id(x) for x in properties]
 
     props_df = pd.DataFrame(list(zip(properties, ids)), columns=["iri", "entity_id"])
     labels_df = pd.DataFrame(labels, columns=["iri", "label"])
@@ -51,8 +98,32 @@ def main():
         desc_df, how="left", on="iri"
     )
 
+    inverse_df = pd.DataFrame(inverses, columns=["iri", "inverse_iri"])
+    if not inverse_df.empty:
+        # Only keep blank-node-free, resolvable pairs; RO states some inverses against
+        # anonymous property expressions, which have no entity id to point at.
+        inverse_df = inverse_df.loc[
+            inverse_df["inverse_iri"].map(lambda x: isinstance(x, rdflib.URIRef))
+        ].copy()
+        inverse_df["inverse_entity_id"] = inverse_df["inverse_iri"].map(
+            iri_to_entity_id
+        )
+        inverse_df = inverse_df.drop("inverse_iri", axis=1).drop_duplicates("iri")
+        ro_df = ro_df.merge(inverse_df, how="left", on="iri")
+    else:
+        ro_df["inverse_entity_id"] = pd.NA
+
+    ro_df["is_symmetric"] = ro_df["iri"].isin(symmetric_property_iris(g))
+
     ro_df = ro_df.drop("iri", axis=1)
 
+    n_pairs = int(ro_df["inverse_entity_id"].notna().sum())
+    n_symmetric = int(ro_df["is_symmetric"].sum())
+    print(
+        f"Extracted {len(ro_df)} properties, {n_pairs} with a declared inverse, "
+        f"{n_symmetric} symmetric"
+    )
+
     ro_df.to_csv("./data/derived/properties.ro.csv", index=False)
 
 
diff --git a/run/preprocessing/merge_properties.py b/run/preprocessing/merge_properties.py
index 681b670..aabc72b 100644
--- a/run/preprocessing/merge_properties.py
+++ b/run/preprocessing/merge_properties.py
@@ -1,7 +1,7 @@
 import pandas as pd
 from pathlib import Path
 
-from pelinker.util import fetch_latest_kb
+from pelinker.kb.registry import fetch_latest_kb
 
 
 def main():
@@ -46,7 +46,14 @@ def main():
     ro_df = ro_df[~mask].copy()
     print(f"number of dropped items in ro_df : {sum(mask)}")
 
+    # RO's `owl:inverseOf` pairs ride along; sources without the column get NA. This is
+    # the KB's own statement that two entries are one relation read from two ends — the
+    # signal a direction model needs. No `orientation` column is materialized: RO does
+    # not name a canonical member of a pair, and inventing one would put an assertion in
+    # the KB that no source makes.
     df = pd.concat([ro_df, present_gg_df, add_gg_df])
+    if "inverse_entity_id" not in df.columns:
+        df["inverse_entity_id"] = pd.NA
 
     # assign id to ad hoc properties
     df_no_id = df[df["entity_id"].isnull()].sort_values("label").copy()
@@ -76,10 +83,21 @@ def main():
     print(df.isnull().sum(0))
     current_version = version + 1
 
-    if set(df["entity_id"]) != reference_ids:
-        df.sort_values(["entity_id", "label"]).to_csv(
-            f"./data/derived/properties.synthesis.{current_version}.csv", index=False
+    # A new version is warranted when the vocabulary changes *or* when the schema does —
+    # the id-only check silently swallowed added columns (e.g. inverse_entity_id).
+    reference_columns = set(reference_df.columns) if fname is not None else set()
+    ids_changed = set(df["entity_id"]) != reference_ids
+    columns_changed = set(df.columns) != reference_columns
+    if ids_changed or columns_changed:
+        out_path = f"./data/derived/properties.synthesis.{current_version}.csv"
+        df.sort_values(["entity_id", "label"]).to_csv(out_path, index=False)
+        print(
+            f"wrote {out_path} (ids changed: {ids_changed}, "
+            f"columns changed: {columns_changed}, "
+            f"{int(df['inverse_entity_id'].notna().sum())} inverse pairs)"
         )
+    else:
+        print("no vocabulary or schema change; not writing a new version")
 
 
 if __name__ == "__main__":
diff --git a/run/test_server.py b/run/smoke_server.py
similarity index 95%
rename from run/test_server.py
rename to run/smoke_server.py
index 0a067b9..44bdcc6 100644
--- a/run/test_server.py
+++ b/run/smoke_server.py
@@ -11,7 +11,8 @@
 import click
 import requests
 
-from pelinker.io import load_json_path
+from pelinker.data import load_json_path
+from pelinker.core.paths import ExpandedPath
 
 _DEFAULT_LINK_BODY: dict[str, Any] = {
     "texts": [
@@ -103,12 +104,14 @@ def _request_json(
 )
 @click.option(
     "--input-path",
-    type=click.Path(path_type=pathlib.Path, exists=True, dir_okay=False, readable=True),
+    type=ExpandedPath(
+        path_type=pathlib.Path, exists=True, dir_okay=False, readable=True
+    ),
     help="JSON body for POST endpoints; must include 'text' or 'texts' (optional thr_score, use_gpu, …).",
 )
 @click.option(
     "--output",
-    type=click.Path(path_type=pathlib.Path),
+    type=ExpandedPath(path_type=pathlib.Path),
     help="Write JSON response to this path instead of printing.",
 )
 @click.option("--timeout", type=float, default=300.0, show_default=True)
diff --git a/test/conftest.py b/test/conftest.py
index da8a563..fa6032e 100644
--- a/test/conftest.py
+++ b/test/conftest.py
@@ -2,15 +2,16 @@
 import spacy
 import pandas as pd
 from importlib.resources import files
-from pelinker.util import load_models, split_text_into_batches
+from pelinker.text.chunking import split_text_into_batches
+from pelinker.text.models import load_models
 
-SPACY_MODEL = "en_core_web_trf"
+SPACY_MODEL = "en_core_web_lg"
 
 
 def _load_spacy_or_skip(name: str):
     """Skip rather than error when the pipeline is not installed locally / in CI.
 
-    ``en_core_web_trf`` is a large out-of-band download (``spacy download``), so a fresh
+    ``en_core_web_lg`` is a large out-of-band download (``spacy download``), so a fresh
     checkout would otherwise report errors that say nothing about the code under test.
     Install it to run these for real; see the ``heavy`` marker in ``pyproject.toml``.
     """
diff --git a/test/test_annotate_decode.py b/test/test_annotate_decode.py
new file mode 100644
index 0000000..22c5b0d
--- /dev/null
+++ b/test/test_annotate_decode.py
@@ -0,0 +1,471 @@
+"""Label-based annotation decoding: canonical vocabulary, label -> id, rejections."""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from pelinker.core.onto import SimplifiedToken
+
+_MODULE_PATH = Path(__file__).resolve().parents[1] / "run" / "eval" / "annotate_llm.py"
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("annotate_llm", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    # Registered before exec: the module defines a dataclass, and dataclasses resolve
+    # field types through sys.modules[cls.__module__].
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+ann = _load_module()
+
+TEXT = "TAMs secrete IL-10, and IL-6 is regulated by TAMs."
+
+
+@pytest.fixture
+def kb() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "entity_id": ["PEL.1", "RO.2", "PEL.3"],
+            "label": ["regulates", "regulated by", "secretes"],
+            "description": ["regulation relation", "converse of regulates", ""],
+            "inverse_entity_id": ["RO.2", "PEL.1", None],
+            "inverse_label": ["regulated by", "regulates", None],
+            "is_canonical": [True, False, True],
+            "canonical_entity_id": ["PEL.1", "PEL.1", "PEL.3"],
+        }
+    )
+
+
+def test_a_kb_without_pair_derivation_is_refused(kb) -> None:
+    """Silently offering both pair members would change what the gold means."""
+    legacy = kb.drop(columns=["is_canonical"])
+
+    with pytest.raises(Exception, match="is_canonical"):
+        ann.canonical_kb(legacy)
+
+
+def test_only_canonical_labels_are_offered(kb) -> None:
+    offered = ann.canonical_kb(kb)
+
+    assert set(offered["label"]) == {"regulates", "secretes"}
+    # The converse member is deliberately absent: one valid encoding per mention.
+    assert "regulated by" not in set(offered["label"])
+
+
+def test_kb_table_carries_no_ids_but_shows_the_converse(kb) -> None:
+    table = ann.kb_table_text(ann.canonical_kb(kb))
+    lines = table.split("\n")
+
+    assert "PEL.1" not in table and "RO.2" not in table
+    assert lines[0] == (
+        '1. "regulates" | def: regulation relation | converse wording: "regulated by"'
+    )
+    # A relation with no converse gets no dangling field.
+    assert lines[1] == '2. "secretes"'
+    # One line per offered relation, whatever the descriptions look like.
+    assert len(lines) == len(ann.canonical_kb(kb))
+
+
+def test_labels_decode_to_ids(kb) -> None:
+    index = ann.build_label_index(ann.canonical_kb(kb))
+
+    hits, rejected = ann.resolve_annotations(
+        TEXT,
+        [
+            {
+                "surface": "secrete",
+                "occurrence": 1,
+                "label": "secretes",
+                "direction": "forward",
+            }
+        ],
+        label_index=index,
+        annotator="llm-a",
+    )
+
+    assert rejected == []
+    assert hits[0]["entity_id"] == "PEL.3"
+    assert hits[0]["label"] == "secretes"
+    assert TEXT[hits[0]["a"] : hits[0]["b"]] == "secrete"
+
+
+def test_label_matching_tolerates_case_and_padding(kb) -> None:
+    index = ann.build_label_index(ann.canonical_kb(kb))
+
+    hits, rejected = ann.resolve_annotations(
+        TEXT,
+        [{"surface": "secrete", "occurrence": 1, "label": "  Secretes "}],
+        label_index=index,
+        annotator="llm-a",
+    )
+
+    assert rejected == []
+    assert hits[0]["entity_id"] == "PEL.3"
+
+
+def test_a_passive_mention_uses_the_canonical_label_with_inverse_direction(kb) -> None:
+    index = ann.build_label_index(ann.canonical_kb(kb))
+
+    hits, _ = ann.resolve_annotations(
+        TEXT,
+        [
+            {
+                "surface": "is regulated by",
+                "occurrence": 1,
+                "label": "regulates",
+                "direction": "inverse",
+            }
+        ],
+        label_index=index,
+        annotator="llm-a",
+    )
+
+    assert hits[0]["entity_id"] == "PEL.1"
+    assert hits[0]["direction"] == "inverse"
+
+
+def test_an_unlisted_label_is_rejected_not_guessed(kb) -> None:
+    index = ann.build_label_index(ann.canonical_kb(kb))
+
+    hits, rejected = ann.resolve_annotations(
+        TEXT,
+        [{"surface": "secrete", "occurrence": 1, "label": "emits"}],
+        label_index=index,
+        annotator="llm-a",
+    )
+
+    assert hits == []
+    assert rejected[0]["reasons"] == ["unknown_label"]
+
+
+def test_the_suppressed_converse_label_is_not_silently_accepted(kb) -> None:
+    """'regulated by' is a real KB label but not offered; using it is an error."""
+    index = ann.build_label_index(ann.canonical_kb(kb))
+
+    hits, rejected = ann.resolve_annotations(
+        TEXT,
+        [{"surface": "is regulated by", "occurrence": 1, "label": "regulated by"}],
+        label_index=index,
+        annotator="llm-a",
+    )
+
+    assert hits == []
+    assert "unknown_label" in rejected[0]["reasons"]
+
+
+# --------------------------------------------------- occurrence + span resolution
+
+
+def test_word_boundaries_are_respected() -> None:
+    """A short answer must not be located inside a longer word."""
+    text = "The drug reduces risk."
+
+    assert ann.iter_occurrences(text, "reduce") == []
+    assert ann.iter_occurrences(text, "reduces") == [(9, 16)]
+
+
+def test_multiword_surface_respects_boundaries() -> None:
+    text = "was correlated to outcome"
+
+    assert ann.iter_occurrences(text, "related to") == []
+    assert ann.iter_occurrences(text, "correlated to") == [(4, 17)]
+
+
+def test_requested_occurrence_is_used_when_it_exists() -> None:
+    text = "binds here and binds there"
+
+    span, fallback = ann.find_occurrence(text, "binds", 2)
+
+    assert span == (15, 20) and fallback is False
+
+
+def test_overcounted_occurrence_falls_back_to_an_unclaimed_one() -> None:
+    """Models miscount repeats; the surface is still required verbatim."""
+    text = "the gene is expressed."
+
+    span, fallback = ann.find_occurrence(text, "expressed", 3)
+
+    assert span == (12, 21) and fallback is True
+
+
+def test_a_claimed_span_is_never_annotated_twice() -> None:
+    text = "the gene is expressed."
+    claimed = {(12, 21)}
+
+    span, fallback = ann.find_occurrence(text, "expressed", 1, claimed=claimed)
+
+    assert span is None and fallback is False
+
+
+def test_nested_spans_collapse_to_the_longest() -> None:
+    hits = [
+        {"a": 10, "b": 19, "label": "expresses"},
+        {"a": 10, "b": 22, "label": "expressed in"},
+        {"a": 40, "b": 45, "label": "binds"},
+    ]
+
+    kept, dropped = ann.drop_nested_spans(hits)
+
+    assert [(h["a"], h["b"]) for h in kept] == [(10, 22), (40, 45)]
+    assert dropped[0]["reasons"] == ["nested_in_longer_span"]
+
+
+# ------------------------------------------------------- converse-label recovery
+
+
+def test_a_converse_label_maps_to_canonical_with_flipped_direction(kb) -> None:
+    """Dropping these would remove inverse-direction mentions specifically."""
+    index = ann.build_label_index(kb)
+
+    hits, rejected = ann.resolve_annotations(
+        "IL-6 is regulated by TAMs.",
+        [
+            {
+                "surface": "regulated by",
+                "occurrence": 1,
+                "label": "regulated by",
+                "direction": "forward",
+            }
+        ],
+        label_index=index,
+        annotator="llm-a",
+    )
+
+    assert rejected == []
+    assert hits[0]["entity_id"] == "PEL.1"  # canonical member
+    assert hits[0]["direction"] == "inverse"  # orientation flipped
+    assert hits[0]["label_canonicalized"] is True
+
+
+def test_flip_direction_leaves_symmetric_and_na_alone() -> None:
+    assert ann.flip_direction("forward") == "inverse"
+    assert ann.flip_direction("inverse") == "forward"
+    assert ann.flip_direction("symmetric") == "symmetric"
+    assert ann.flip_direction(None) is None
+
+
+def test_an_oriented_answer_on_a_symmetric_relation_is_coerced_and_flagged() -> None:
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["RO.34"],
+            "label": ["interacts with"],
+            "is_symmetric": [True],
+            "is_canonical": [True],
+            "canonical_entity_id": ["RO.34"],
+        }
+    )
+
+    hits, rejected = ann.resolve_annotations(
+        "p53 interacts with MDM2.",
+        [
+            {
+                "surface": "interacts with",
+                "occurrence": 1,
+                "label": "interacts with",
+                "direction": "forward",
+            }
+        ],
+        label_index=ann.build_label_index(kb),
+        annotator="llm-a",
+        symmetric=ann.symmetric_ids(kb),
+    )
+
+    assert rejected == []
+    assert hits[0]["direction"] == "symmetric"
+    assert hits[0]["direction_coerced"] is True
+
+
+def test_a_symmetric_answer_needs_no_coercion() -> None:
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["RO.34"],
+            "label": ["interacts with"],
+            "is_symmetric": [True],
+            "is_canonical": [True],
+            "canonical_entity_id": ["RO.34"],
+        }
+    )
+
+    hits, _ = ann.resolve_annotations(
+        "p53 interacts with MDM2.",
+        [
+            {
+                "surface": "interacts with",
+                "occurrence": 1,
+                "label": "interacts with",
+                "direction": "symmetric",
+            }
+        ],
+        label_index=ann.build_label_index(kb),
+        annotator="llm-a",
+        symmetric=ann.symmetric_ids(kb),
+    )
+
+    assert hits[0]["direction"] == "symmetric"
+    assert "direction_coerced" not in hits[0]
+
+
+def test_a_nominal_surface_on_a_verb_label_is_rejected(kb) -> None:
+    """Training sees verb mentions only; a noun in gold scores a skill never taught."""
+    text = "The association of IL-6 with TAMs regulates growth."
+    item = {"surface": "association", "occurrence": 1, "label": "regulates"}
+
+    hits, rejected = ann.resolve_annotations(
+        text,
+        [item],
+        label_index=ann.build_label_index(kb),
+        annotator="llm-a",
+        verb_labels=frozenset({"regulates"}),
+        has_verb=lambda a, b: False,
+    )
+
+    assert hits == []
+    assert rejected[0]["reasons"] == ["non_verbal"]
+
+
+def test_a_non_verb_label_keeps_its_noun_surface(kb) -> None:
+    hits, rejected = ann.resolve_annotations(
+        "TAMs secrete IL-10.",
+        [{"surface": "secrete", "occurrence": 1, "label": "secretes"}],
+        label_index=ann.build_label_index(kb),
+        annotator="llm-a",
+        verb_labels=frozenset({"regulates"}),  # "secretes" is not in the verb set here
+        has_verb=lambda a, b: False,
+    )
+
+    assert rejected == [] and len(hits) == 1
+
+
+def _tok(ix: int, text: str, lemma: str) -> SimplifiedToken:
+    return SimplifiedToken(ix=ix, ix_end=ix + len(text), text=text, lemma=lemma, tag="")
+
+
+def test_anchor_check_tells_label_wording_from_paraphrase() -> None:
+    # "Smoking leads to cancer and causes harm."
+    tokens = [
+        _tok(0, "Smoking", "smoking"),
+        _tok(8, "leads", "lead"),
+        _tok(14, "to", "to"),
+        _tok(17, "cancer", "cancer"),
+        _tok(24, "and", "and"),
+        _tok(28, "causes", "cause"),
+        _tok(35, "harm", "harm"),
+    ]
+    anchors = {"PEL.4": [frozenset({"cause"})]}
+    anchored = ann.anchor_check(tokens, anchors)
+
+    assert anchored(28, 34, "PEL.4") is True  # "causes"
+    assert anchored(8, 16, "PEL.4") is False  # "leads to": a paraphrase
+    assert anchored(28, 34, "PEL.unknown") is False
+
+
+def test_resolved_hits_carry_the_anchor_flag(kb) -> None:
+    hits, _ = ann.resolve_annotations(
+        "TAMs secrete IL-10.",
+        [{"surface": "secrete", "occurrence": 1, "label": "secretes"}],
+        label_index=ann.build_label_index(kb),
+        annotator="llm-a",
+        is_anchored=lambda a, b, entity_id: entity_id == "PEL.3",
+    )
+
+    assert hits[0]["surface_anchored"] is True
+
+
+# ------------------------------------------------------------- response handling
+
+
+def test_an_object_wrapping_one_array_is_unwrapped() -> None:
+    """Object-only JSON modes wrap the requested array; the records are what counts."""
+    raw = '{"annotations": [{"surface": "binds", "occurrence": 1}]}'
+
+    assert ann.parse_llm_response(raw) == [{"surface": "binds", "occurrence": 1}]
+
+
+def test_an_object_that_is_not_a_wrapper_is_still_refused() -> None:
+    with pytest.raises(ValueError, match="JSON array"):
+        ann.parse_llm_response('{"surface": "binds", "occurrence": 1}')
+
+
+def test_a_reemitted_mention_is_reported_as_a_duplicate(kb) -> None:
+    """Quoting text that is absent and repeating a mention are different failures."""
+    item = {"surface": "secrete", "occurrence": 1, "label": "secretes"}
+
+    hits, rejected = ann.resolve_annotations(
+        TEXT,
+        [item, dict(item), {**item, "surface": "secreted"}],
+        label_index=ann.build_label_index(kb),
+        annotator="llm-a",
+    )
+
+    assert len(hits) == 1
+    assert [r["reasons"] for r in rejected] == [
+        ["duplicate_occurrence"],
+        ["surface_not_found"],
+    ]
+
+
+def test_a_truncated_document_is_reported_and_left_out(
+    kb, nlp, tmp_path: Path, monkeypatch
+) -> None:
+    """An unanswered document must not read as one where the annotator found nothing."""
+    import json
+
+    from click.testing import CliRunner
+
+    from pelinker.eval.llm import LLMIncompleteError
+
+    kb_path = tmp_path / "kb.csv"
+    kb.assign(needs_review=False, is_symmetric=False).to_csv(kb_path, index=False)
+    sample = tmp_path / "gold"
+    sample.mkdir()
+    pd.DataFrame({"doc_id": [1, 2], "role": ["primary", "primary"]}).to_csv(
+        sample / "sample_manifest.csv", index=False
+    )
+    (sample / "sample_texts.jsonl").write_text(
+        "\n".join(json.dumps({"doc_id": i, "text": TEXT}) for i in (1, 2)),
+        encoding="utf-8",
+    )
+
+    calls: list[str] = []
+
+    def fake_complete(*, user, **kw):
+        calls.append(user)
+        if len(calls) > 1:
+            raise LLMIncompleteError("stopped at max_output_tokens")
+        return '[{"surface": "secrete", "occurrence": 1, "label": "secretes"}]'
+
+    monkeypatch.setattr(ann, "complete", fake_complete)
+    monkeypatch.setattr(ann.spacy, "load", lambda name: nlp)
+    out = tmp_path / "gold.llm-b.json"
+
+    result = CliRunner().invoke(
+        ann.main,
+        [
+            "--sample-dir",
+            str(sample),
+            "--kb-csv-path",
+            str(kb_path),
+            "--output-path",
+            str(out),
+            "--roles",
+            "primary",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    docs = json.loads(out.read_text(encoding="utf-8"))
+    assert [d["doc_id"] for d in docs] == [1]
+    report = json.loads(out.with_suffix(".report.json").read_text(encoding="utf-8"))
+    assert report["n_truncated"] == 1
+    assert [
+        r["doc_id"] for r in report["rejected"] if r["reasons"] == ["truncated"]
+    ] == [2]
diff --git a/test/test_audit_sample.py b/test/test_audit_sample.py
new file mode 100644
index 0000000..92f8892
--- /dev/null
+++ b/test/test_audit_sample.py
@@ -0,0 +1,128 @@
+"""Sample audit: eligibility re-check, empty documents, concentration, coverage curve."""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+_MODULE_PATH = Path(__file__).resolve().parents[1] / "run" / "eval" / "audit_sample.py"
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("audit_sample", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+au = _load_module()
+
+KB = pd.DataFrame(
+    {
+        "entity_id": ["PEL.1", "RO.2", "PEL.3"],
+        "label": ["regulates", "regulated by", "secretes"],
+        "canonical_entity_id": ["PEL.1", "PEL.1", "PEL.3"],
+    }
+)
+MANIFEST = pd.DataFrame(
+    {
+        "doc_id": [1, 2, 3],
+        "publication_year": [2015, 2015, 1980],
+        "stratum": ["y0-high-tail", "y0-none", "y0-low-common"],
+        "batch": [0, 0, 1],
+        "n_verb_mentions": [3, 0, 1],
+    }
+)
+GOLD = {
+    1: [{"entity_id": "PEL.1"}, {"entity_id": "PEL.1"}],
+    2: [{"entity_id": "PEL.3"}],
+    3: [],
+}
+
+
+def test_converse_ids_count_under_the_canonical_label() -> None:
+    by_id, by_label = au.canonical_label_map(KB)
+
+    assert by_id["RO.2"] == "regulates"
+    assert by_label["regulated by"] == "regulates"
+
+
+def test_gold_section_flags_empty_documents_and_hit_negatives() -> None:
+    by_id, _ = au.canonical_label_map(KB)
+
+    section, counts = au.gold_section(MANIFEST, GOLD, by_id, min_mentions=2)
+
+    assert section["documents_without_hits"] == 1
+    assert section["negative_controls_with_hits"] == 1
+    assert section["labels_at_min_mentions"] == 1
+    assert counts.to_dict() == {"regulates": 2, "secretes": 1}
+
+
+def test_coverage_curve_is_cumulative_over_batches() -> None:
+    by_id, by_label = au.canonical_label_map(KB)
+    _, counts = au.gold_section(MANIFEST, GOLD, by_id, min_mentions=2)
+    pool = pd.Series(["regulated by|secretes", "regulates", "secretes", ""])
+
+    cov = au.coverage_section(MANIFEST, GOLD, counts, pool, by_id, by_label, 2)
+
+    assert cov["reachable_labels"] == 2
+    assert cov["covered_reachable"] == 1
+    assert [c["distinct_labels"] for c in cov["curve"]] == [2, 2]
+
+
+def test_quality_section_reapplies_the_gate() -> None:
+    texts = {1: "short", 2: "short", 3: "short"}
+
+    section = au.quality_section(MANIFEST, texts)
+
+    assert section["eligibility"] == {"too_short": 2, "year_before_min": 1}
+
+
+def test_documents_absent_from_the_gold_file_are_not_scored(tmp_path: Path) -> None:
+    """Reserve documents are never annotated; they must not count as 'without hits'."""
+    import json
+
+    from click.testing import CliRunner
+
+    manifest = MANIFEST.assign(role=["primary", "primary", "reserve"])
+    manifest.to_csv(tmp_path / "sample_manifest.csv", index=False)
+    (tmp_path / "sample_texts.jsonl").write_text(
+        "".join(json.dumps({"doc_id": d, "text": "x"}) + "\n" for d in (1, 2, 3)),
+        encoding="utf-8",
+    )
+    gold = [{"doc_id": d, "ground_truth": GOLD[d]} for d in (1, 2)]
+    (tmp_path / "gold.llm-a.json").write_text(json.dumps(gold), encoding="utf-8")
+    kb_path = tmp_path / "kb.csv"
+    KB.to_csv(kb_path, index=False)
+
+    result = CliRunner().invoke(
+        au.main, ["--gold-dir", str(tmp_path), "--kb-csv-path", str(kb_path)]
+    )
+
+    assert result.exit_code == 0, result.output
+    audit = json.loads((tmp_path / "sample_audit.json").read_text())
+    assert audit["documents_not_in_gold_file"] == 1
+    assert audit["gold"]["documents"] == 2
+    assert audit["gold"]["documents_without_hits"] == 0
+
+
+def test_paraphrase_share_counts_unanchored_hits() -> None:
+    by_id, _ = au.canonical_label_map(KB)
+    gold = {
+        1: [
+            {"entity_id": "PEL.1", "surface_anchored": True},
+            {"entity_id": "PEL.1", "surface_anchored": False},
+        ],
+        2: [{"entity_id": "PEL.3", "surface_anchored": True}],
+        3: [],
+    }
+
+    section, _ = au.gold_section(MANIFEST, gold, by_id, min_mentions=2)
+
+    assert section["paraphrase_hits"] == 1
+    assert section["paraphrase_share"] == round(1 / 3, 3)
diff --git a/test/test_chunking_layers.py b/test/test_chunking_layers.py
index ac417a7..6afc03e 100644
--- a/test/test_chunking_layers.py
+++ b/test/test_chunking_layers.py
@@ -2,7 +2,8 @@
 
 import pytest
 
-from pelinker.util import normalize_layers_spec, split_text_into_token_budget
+from pelinker.text.chunking import split_text_into_token_budget
+from pelinker.text.models import normalize_layers_spec
 
 
 def test_normalize_layers_spec_digit_string() -> None:
diff --git a/test/test_class_view_objective.py b/test/test_class_view_objective.py
new file mode 100644
index 0000000..7979f0d
--- /dev/null
+++ b/test/test_class_view_objective.py
@@ -0,0 +1,265 @@
+"""The selection objective and the fit score agreement in the configured class view."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, cast
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from pelinker.clustering.fit import fit_hdbscan_on_umap
+from pelinker.clustering.grid import evaluate_cluster_size_grid
+from pelinker.core.config import (
+    ClusteringOptimizationConfig,
+    EmbeddingModelMetadata,
+    EmbeddingSourceSpec,
+    LinkerFitConfig,
+    ManifoldOovScreenerConfig,
+    TransformConfig,
+)
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.kb.classes import ENTITY_CLASS_COLUMN, ClassView, file_sha256
+from pelinker.kb.kb_out import kb_out_to_reldir_map
+from pelinker.model import Linker
+from pelinker.search.selection import apply_class_view, load_selection_frame
+
+
+def _pairs_kb(tmp_path: Path) -> Path:
+    path = tmp_path / "kb.pairs.csv"
+    pd.DataFrame(
+        {
+            "entity_id": ["RO.1", "RO.2", "PEL.3"],
+            "label": ["regulates", "regulated by", "inhibits"],
+            "is_canonical": [True, False, True],
+            "canonical_entity_id": ["RO.1", "RO.1", "PEL.3"],
+            "is_symmetric": [False, False, False],
+        }
+    ).to_csv(path, index=False)
+    return path
+
+
+def _two_voice_blobs(n: int = 30) -> pd.DataFrame:
+    """Two tight blobs: active ``inhibits`` and passive ``inhibits``.
+
+    The KB has no converse entry for ``inhibits``, so both blobs carry the same raw label
+    and differ only in ``direction`` — the case where raw labels hide the voice split.
+    """
+    rng = np.random.default_rng(3)
+    a = rng.normal(0.0, 0.05, size=(n, 2))
+    b = rng.normal(5.0, 0.05, size=(n, 2))
+    return pd.DataFrame(
+        {
+            "u0": np.concatenate([a[:, 0], b[:, 0]]),
+            "u1": np.concatenate([a[:, 1], b[:, 1]]),
+            "entity": ["inhibits"] * (2 * n),
+            "direction": ["forward"] * n + ["inverse"] * n,
+        }
+    )
+
+
+def test_grid_ari_follows_the_class_view(tmp_path: Path) -> None:
+    frame = _two_voice_blobs()
+    cfg = ClusteringOptimizationConfig(
+        class_view="reldir", class_kb_path=str(_pairs_kb(tmp_path))
+    )
+
+    raw = evaluate_cluster_size_grid(frame[["u0", "u1", "entity"]], ["u0", "u1"], [5])
+    viewed_frame = apply_class_view(frame, cfg)
+    viewed = evaluate_cluster_size_grid(
+        viewed_frame[["u0", "u1", "entity", ENTITY_CLASS_COLUMN]], ["u0", "u1"], [5]
+    )
+
+    # One raw class split into two clusters scores zero; in reldir the split is exact.
+    assert raw["ari"].iloc[0] == pytest.approx(0.0, abs=1e-9)
+    assert viewed["ari"].iloc[0] == pytest.approx(1.0)
+
+
+def test_fit_metrics_ari_follows_the_class_view(tmp_path: Path) -> None:
+    frame = apply_class_view(
+        _two_voice_blobs(),
+        ClusteringOptimizationConfig(
+            class_view="reldir", class_kb_path=str(_pairs_kb(tmp_path))
+        ),
+    )
+    umap = frame[["u0", "u1"]].to_numpy(dtype=np.float32)
+
+    viewed = fit_hdbscan_on_umap(umap, frame, 5).fit_metrics
+    raw = fit_hdbscan_on_umap(umap, frame.drop(columns=[ENTITY_CLASS_COLUMN]), 5)
+
+    assert viewed.ari == pytest.approx(1.0)
+    assert raw.fit_metrics.ari == pytest.approx(0.0, abs=1e-9)
+
+
+def test_a_view_without_a_kb_is_refused() -> None:
+    with pytest.raises(ValueError, match="needs class_kb_path"):
+        apply_class_view(
+            _two_voice_blobs(), ClusteringOptimizationConfig(class_view="rel")
+        )
+
+
+def test_raw_view_leaves_the_frame_untouched() -> None:
+    frame = _two_voice_blobs()
+    out = apply_class_view(frame, ClusteringOptimizationConfig())
+    assert out is frame
+
+
+def _embedding_frame(dim: int = 12, n: int = 30, n_neg: int = 40) -> pd.DataFrame:
+    """Three separated blobs: active regulates, passive regulates (its converse entry),
+    active inhibits; plus synthetic negatives."""
+    rng = np.random.default_rng(11)
+    rows: list[dict[str, object]] = []
+    blobs = [
+        ("regulates", "forward", 0),
+        ("regulated by", "forward", 1),
+        ("inhibits", "forward", 2),
+    ]
+    for label, direction, axis in blobs:
+        centre = np.zeros(dim, dtype=np.float32)
+        centre[axis] = 6.0
+        for i in range(n):
+            rows.append(
+                {
+                    "pmid": f"{label}-{i // 3}",
+                    "entity": label,
+                    "mention": f"{label}-{i}",
+                    "direction": direction,
+                    "embed": (centre + rng.normal(0, 0.1, dim)).astype(np.float32),
+                }
+            )
+    for j in range(n_neg):
+        rows.append(
+            {
+                "pmid": f"neg-{j}",
+                "entity": NEGATIVE_LABEL,
+                "mention": f"n{j}",
+                "direction": None,
+                "embed": rng.normal(0, 1.0, dim).astype(np.float32) - 4.0,
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def _fit(tmp_path: Path, *, class_view: ClassView) -> Linker:
+    kb_path = _pairs_kb(tmp_path)
+    parquet = tmp_path / f"emb-{class_view}.parquet"
+    _embedding_frame().to_parquet(parquet)
+    tc = TransformConfig(
+        pca_components=8,
+        umap_components=3,
+        cluster_viz_components=3,
+        pca_seed=13,
+        umap_seed=13,
+    )
+    linker = Linker(
+        labels_map={"RO.1": "regulates", "RO.2": "regulated by", "PEL.3": "inhibits"},
+        transform_config=tc,
+        embedding_metadata=EmbeddingModelMetadata(
+            sources=(EmbeddingSourceSpec(model_type="t", layers_spec="1"),)
+        ),
+    )
+    linker.fit(
+        embeddings=parquet,
+        transform_config=tc,
+        min_cluster_size=5,
+        fit_config=LinkerFitConfig(
+            base_seed=13,
+            screener_seed=13,
+            projection_screener=ManifoldOovScreenerConfig(enabled=False),
+            predict_mode="legacy",
+            class_view=class_view,
+            class_kb_path=None if class_view == "raw" else str(kb_path),
+        ),
+    )
+    return linker
+
+
+def _component_labels(linker: Linker) -> set[str]:
+    assert linker.kb_out_catalog is not None
+    catalog = cast(dict[str, Any], linker.kb_out_catalog)
+    return {
+        c["entity"] for cluster in catalog["clusters"] for c in cluster["components"]
+    }
+
+
+def test_reldir_fit_reads_composition_between_relations_with_a_direction(
+    tmp_path: Path,
+) -> None:
+    linker = _fit(tmp_path, class_view="reldir")
+
+    # The converse entry never appears as its own entity: its mentions are regulates.
+    assert _component_labels(linker) == {"regulates", "inhibits"}
+    assert sorted(set(linker.cluster_direction.values())) == ["forward", "inverse"]
+
+    assert linker.kb_out_catalog is not None
+    catalog = cast(dict[str, Any], linker.kb_out_catalog)
+    fit_prov = catalog["provenance"]["fit"]
+    assert fit_prov["class_view"] == "reldir"
+    assert fit_prov["class_kb_sha256"] == file_sha256(tmp_path / "kb.pairs.csv")
+
+    bridge = kb_out_to_reldir_map(catalog)
+    assert ("RO.1", "inverse") in set(bridge.values())
+    assert ("RO.1", "forward") in set(bridge.values())
+
+
+def test_raw_fit_keeps_matched_labels_and_no_direction(tmp_path: Path) -> None:
+    linker = _fit(tmp_path, class_view="raw")
+
+    assert "regulated by" in _component_labels(linker)
+    assert linker.cluster_direction == {}
+
+
+def test_load_selection_frame_applies_the_view(tmp_path: Path) -> None:
+    parquet = tmp_path / "emb.parquet"
+    _embedding_frame().to_parquet(parquet)
+    cfg = ClusteringOptimizationConfig(
+        class_view="reldir", class_kb_path=str(_pairs_kb(tmp_path))
+    )
+
+    loaded = load_selection_frame(file_path=parquet, config=cfg)
+
+    assert loaded is not None
+    kb_rows = loaded.loc[loaded["entity"] != NEGATIVE_LABEL]
+    assert set(kb_rows[ENTITY_CLASS_COLUMN]) == {
+        "regulates|forward",
+        "regulates|inverse",
+        "inhibits|forward",
+    }
+
+
+def _fingerprint(
+    class_view: ClassView = "raw", class_kb_path: Path | None = None
+) -> dict[str, Any]:
+    from pelinker.search.checkpoint_base import shared_fingerprint_fields
+
+    return shared_fingerprint_fields(
+        cluster_viz_method="pca",
+        min_class_size=20,
+        seed=13,
+        pca_seed=13,
+        umap_seed=None,
+        clustering_sample_rows=None,
+        batch_size=1000,
+        n_sample=3,
+        selected_labels_kb_path=None,
+        max_scale=100,
+        class_view=class_view,
+        class_kb_path=class_kb_path,
+    )
+
+
+def test_a_raw_run_keeps_the_fingerprint_it_had_before_views_existed() -> None:
+    assert _fingerprint() == _fingerprint(class_view="raw")
+    assert "class_view" not in _fingerprint()
+
+
+def test_a_view_enters_the_fingerprint_by_kb_content(tmp_path: Path) -> None:
+    kb_path = _pairs_kb(tmp_path)
+    viewed = _fingerprint(class_view="reldir", class_kb_path=kb_path)
+    assert viewed["class_view"] == "reldir"
+    assert viewed["class_kb_sha256"] == file_sha256(kb_path)
+
+    # The same path with different content is a different run.
+    kb_path.write_text(kb_path.read_text() + "PEL.9,binds,False,PEL.9,False\n")
+    assert _fingerprint(class_view="reldir", class_kb_path=kb_path) != viewed
diff --git a/test/test_cli_path_expansion.py b/test/test_cli_path_expansion.py
new file mode 100644
index 0000000..38e8478
--- /dev/null
+++ b/test/test_cli_path_expansion.py
@@ -0,0 +1,123 @@
+"""Tilde and env-var expansion for CLI path options.
+
+A shell expands ``~`` before the process ever sees it. Anything that builds argv directly
+— an IDE run configuration, a systemd unit, ``subprocess`` with a list — does not, so a
+literal ``~/data/x`` reached click and failed ``exists=True`` against a path that exists.
+Unexpanded *output* paths are worse than an error: they create a directory named ``~``.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import click
+import pytest
+from click.testing import CliRunner
+
+from pelinker.core.paths import ExpandedPath, expand_config_path
+
+
+@pytest.fixture
+def home(tmp_path: Path, monkeypatch) -> Path:
+    monkeypatch.setenv("HOME", str(tmp_path))
+    monkeypatch.setenv("USERPROFILE", str(tmp_path))  # Windows equivalent
+    return tmp_path
+
+
+def test_expand_config_path_handles_tilde_and_vars(home: Path, monkeypatch) -> None:
+    monkeypatch.setenv("PELINKER_TEST_DIR", "sub")
+
+    assert expand_config_path("~/x.csv") == home / "x.csv"
+    assert expand_config_path("~/$PELINKER_TEST_DIR/x.csv") == home / "sub" / "x.csv"
+    assert expand_config_path(None) is None
+
+
+def test_existing_file_is_accepted_through_a_tilde(home: Path) -> None:
+    target = home / "data" / "kb.csv"
+    target.parent.mkdir(parents=True)
+    target.write_text("entity_id,label\n", encoding="utf-8")
+
+    @click.command()
+    @click.option("--kb", type=ExpandedPath(exists=True))
+    def cli(kb: str) -> None:
+        click.echo(kb)
+
+    result = CliRunner().invoke(cli, ["--kb", "~/data/kb.csv"])
+
+    assert result.exit_code == 0, result.output
+    assert result.output.strip() == str(target)
+
+
+def test_plain_click_path_would_have_rejected_it(home: Path) -> None:
+    """Pins why the custom type exists rather than the stock one."""
+    target = home / "data" / "kb.csv"
+    target.parent.mkdir(parents=True)
+    target.write_text("x", encoding="utf-8")
+
+    @click.command()
+    @click.option("--kb", type=click.Path(exists=True))
+    def cli(kb: str) -> None:  # pragma: no cover - the invocation fails first
+        click.echo(kb)
+
+    result = CliRunner().invoke(cli, ["--kb", "~/data/kb.csv"])
+
+    assert result.exit_code != 0
+    assert "does not exist" in result.output
+
+
+def test_output_path_expands_instead_of_creating_a_tilde_directory(home: Path) -> None:
+    @click.command()
+    @click.option("--out", type=ExpandedPath())
+    def cli(out: str) -> None:
+        Path(out).parent.mkdir(parents=True, exist_ok=True)
+        Path(out).write_text("ok", encoding="utf-8")
+
+    result = CliRunner().invoke(cli, ["--out", "~/results/gold.json"])
+
+    assert result.exit_code == 0, result.output
+    assert (home / "results" / "gold.json").read_text(encoding="utf-8") == "ok"
+    assert not Path("~").exists()  # never a literal tilde directory
+
+
+def test_absolute_and_relative_paths_are_untouched(home: Path, tmp_path: Path) -> None:
+    target = tmp_path / "plain.csv"
+    target.write_text("x", encoding="utf-8")
+
+    @click.command()
+    @click.option("--p", type=ExpandedPath(exists=True))
+    def cli(p: str) -> None:
+        click.echo(p)
+
+    result = CliRunner().invoke(cli, ["--p", str(target)])
+
+    assert result.exit_code == 0, result.output
+    assert result.output.strip() == str(target)
+
+
+def test_path_type_conversion_still_applies(home: Path) -> None:
+    target = home / "kb.csv"
+    target.write_text("x", encoding="utf-8")
+    seen: list[object] = []
+
+    @click.command()
+    @click.option("--kb", type=ExpandedPath(exists=True, path_type=Path))
+    def cli(kb: Path) -> None:
+        seen.append(kb)
+
+    result = CliRunner().invoke(cli, ["--kb", "~/kb.csv"])
+
+    assert result.exit_code == 0, result.output
+    assert isinstance(seen[0], Path) and seen[0] == target
+
+
+def test_every_cli_path_option_uses_the_expanding_type() -> None:
+    """Guards against a new script reintroducing the bug with stock click.Path."""
+    root = Path(__file__).resolve().parents[1]
+    offenders = []
+    for directory in ("run", "pelinker/cli"):
+        for path in (root / directory).rglob("*.py"):
+            text = path.read_text(encoding="utf-8")
+            if "click.Path(" in text:
+                offenders.append(str(path.relative_to(root)))
+
+    assert not offenders, f"use ExpandedPath instead of click.Path in: {offenders}"
diff --git a/test/test_cluster_composition.py b/test/test_cluster_composition.py
index 3deb205..264576b 100644
--- a/test/test_cluster_composition.py
+++ b/test/test_cluster_composition.py
@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from pelinker.config import ClusterCompositionSnapshot
+from pelinker.core.config import ClusterCompositionSnapshot
 from pelinker.model import (
     cluster_composition_from_training_frame,
     consensus_cluster_names,
diff --git a/test/test_cluster_composition_viz.py b/test/test_cluster_composition_viz.py
index 1297d36..c847a4c 100644
--- a/test/test_cluster_composition_viz.py
+++ b/test/test_cluster_composition_viz.py
@@ -3,7 +3,7 @@
 import pandas as pd
 import pytest
 
-from pelinker.cluster_composition_viz import (
+from pelinker.clustering.composition import (
     HDBSCAN_NOISE_CLUSTER_ID,
     aggregate_cluster_entity_mass,
     build_cluster_composition_df,
diff --git a/test/test_cluster_stability.py b/test/test_cluster_stability.py
new file mode 100644
index 0000000..520200a
--- /dev/null
+++ b/test/test_cluster_stability.py
@@ -0,0 +1,137 @@
+"""Cluster-identity stability across bootstrap draws."""
+
+from __future__ import annotations
+
+import pytest
+
+from pelinker.clustering.stability import (
+    analyze_stability,
+    jaccard,
+    match_two_draws,
+)
+
+
+def _draw(**groups: list[int]) -> dict[int, int]:
+    """``cluster_name=[row, ...]`` → ``row -> cluster_id`` (names must be ``c<int>``)."""
+    out: dict[int, int] = {}
+    for name, rows in groups.items():
+        cid = int(name[1:])
+        for row in rows:
+            out[row] = cid
+    return out
+
+
+def test_jaccard_basics() -> None:
+    assert jaccard({1, 2, 3}, {1, 2, 3}) == 1.0
+    assert jaccard({1, 2}, {3, 4}) == 0.0
+    assert jaccard(set(), set()) == 0.0
+    assert jaccard({1, 2, 3, 4}, {3, 4}) == 0.5
+
+
+def test_identical_draws_match_perfectly() -> None:
+    a = _draw(c0=[1, 2, 3], c1=[4, 5, 6])
+
+    m = match_two_draws(a, dict(a))
+
+    assert m.n_shared_rows == 6
+    assert {(x, y) for x, y, _ in m.matches} == {(0, 0), (1, 1)}
+    assert all(j == 1.0 for _, _, j in m.matches)
+    assert m.ari_shared == 1.0
+
+
+def test_renumbered_clusters_still_match() -> None:
+    """Cluster ids are arbitrary per fit; identity is membership, not the label."""
+    a = _draw(c0=[1, 2, 3], c1=[4, 5, 6])
+    b = _draw(c7=[4, 5, 6], c9=[1, 2, 3])
+
+    m = match_two_draws(a, b)
+
+    assert {(x, y) for x, y, _ in m.matches} == {(0, 9), (1, 7)}
+    assert m.ari_shared == 1.0
+
+
+def test_noise_rows_are_excluded() -> None:
+    a = _draw(c0=[1, 2, 3])
+    a[99] = -1
+    b = _draw(c0=[1, 2, 3])
+    b[99] = -1
+
+    m = match_two_draws(a, b)
+
+    assert m.n_shared_rows == 3  # the noise row is not compared
+    assert m.matches[0][2] == 1.0
+
+
+def test_only_shared_rows_are_compared() -> None:
+    """Bootstrap draws overlap partially; unseen rows must not read as churn."""
+    a = _draw(c0=[1, 2, 3, 4])
+    b = _draw(c0=[3, 4, 5, 6])
+
+    m = match_two_draws(a, b)
+
+    assert m.n_shared_rows == 2
+    # On the shared rows {3,4} the two clusters agree completely.
+    assert m.matches[0][2] == 1.0
+
+
+def test_a_split_cluster_shows_partial_overlap() -> None:
+    a = _draw(c0=[1, 2, 3, 4])
+    b = _draw(c0=[1, 2], c1=[3, 4])
+
+    m = match_two_draws(a, b)
+
+    assert len(m.matches) == 1  # one-to-one: c0 can only claim one half
+    assert m.matches[0][2] == 0.5
+    assert m.ari_shared is not None and m.ari_shared < 1.0
+
+
+def test_stability_report_over_three_stable_draws() -> None:
+    draws = [_draw(c0=[1, 2, 3], c1=[4, 5, 6]) for _ in range(3)]
+
+    report = analyze_stability(draws).to_jsonable()
+
+    assert report["n_draws"] == 3
+    assert report["n_pairs"] == 3
+    assert report["cluster_count_mean"] == 2.0
+    assert report["cluster_count_std"] == 0.0
+    assert report["mean_pair_jaccard"] == 1.0
+    assert report["mean_cross_draw_ari"] == 1.0
+    assert report["persistent_fraction"] == 1.0
+
+
+def test_stability_report_detects_churn_at_a_stable_count() -> None:
+    """The failure the cluster count cannot see: same k, different members.
+
+    Four clusters, not two — with few clusters, chance overlap alone sits near the
+    default floor, so a churn fixture has to be wide enough to separate signal from it.
+    """
+    draws = [
+        _draw(c0=[1, 2, 3], c1=[4, 5, 6], c2=[7, 8, 9], c3=[10, 11, 12]),
+        _draw(c0=[1, 4, 7], c1=[2, 5, 10], c2=[3, 8, 11], c3=[6, 9, 12]),
+        _draw(c0=[1, 5, 11], c1=[3, 6, 7], c2=[2, 9, 10], c3=[4, 8, 12]),
+    ]
+
+    report = analyze_stability(draws).to_jsonable()
+
+    assert report["cluster_count_std"] == 0.0  # count is perfectly stable
+    assert report["mean_pair_jaccard"] < 0.35  # identity is not
+    assert report["persistent_fraction"] == 0.0
+    assert report["mean_cross_draw_ari"] < 0.1
+
+
+def test_at_least_two_draws_are_required() -> None:
+    with pytest.raises(ValueError, match="at least two draws"):
+        analyze_stability([_draw(c0=[1, 2])])
+
+
+def test_per_cluster_persistence_is_reported_for_the_reference_draw() -> None:
+    stable = _draw(c0=[1, 2, 3], c1=[4, 5, 6])
+    churned = _draw(c0=[1, 2, 3], c1=[7, 8, 9])
+
+    report = analyze_stability([stable, churned], jaccard_floor=0.5)
+
+    assert report.per_cluster[0]["mean_jaccard"] == 1.0
+    assert report.per_cluster[0]["match_rate"] == 1.0
+    # Cluster 1's members are gone in the second draw: no surviving identity.
+    assert report.per_cluster[1]["mean_jaccard"] == 0.0
+    assert report.per_cluster[1]["match_rate"] == 0.0
diff --git a/test/test_cluster_stability_cli.py b/test/test_cluster_stability_cli.py
new file mode 100644
index 0000000..a6c46d5
--- /dev/null
+++ b/test/test_cluster_stability_cli.py
@@ -0,0 +1,95 @@
+"""Loading persisted bootstrap labels into stability draws."""
+
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+_MODULE_PATH = (
+    Path(__file__).resolve().parents[1] / "run" / "analysis" / "cluster_stability.py"
+)
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("cluster_stability_cli", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+cli = _load_module()
+
+
+def _labels(**overrides) -> pd.DataFrame:
+    base = pd.DataFrame(
+        {
+            "pmid": ["p1", "p1", "p2", "p1", "p1", "p2"],
+            "itext": [0, 0, 0, 0, 0, 0],
+            "a_abs": [10, 40, 10, 10, 40, 10],
+            "b_abs": [15, 45, 15, 15, 45, 15],
+            "entity": ["x", "y", "x", "x", "y", "x"],
+            "cluster": [0, 1, 0, 0, 1, 0],
+            "sample_idx": [0, 0, 0, 1, 1, 1],
+            "pca_components": [50, 50, 50, 50, 50, 50],
+            "umap_dim": [4, 4, 4, 4, 4, 4],
+        }
+    )
+    for key, value in overrides.items():
+        base[key] = value
+    return base
+
+
+def test_row_keys_separate_repeated_mentions_in_one_document() -> None:
+    frame = _labels()
+
+    keys = cli.row_keys(frame)
+
+    # Two mentions in p1 differ by offset, so they must not collapse to one key.
+    assert keys.nunique() == 3
+    assert keys.iloc[0] != keys.iloc[1]
+
+
+def test_draws_are_split_by_sample_index() -> None:
+    draws = cli.draws_from_labels(_labels(), pca_components=None, umap_dim=None)
+
+    assert len(draws) == 2
+    assert len(draws[0]) == 3
+    assert set(draws[0]) == set(draws[1])  # same rows, comparable across draws
+
+
+def test_mixed_cells_are_rejected_rather_than_silently_matched() -> None:
+    """Clusters from different (pca, umap) cells are not the same objects."""
+    frame = pd.concat([_labels(), _labels(umap_dim=8)], ignore_index=True)
+
+    with pytest.raises(Exception, match="umap_dim"):
+        cli.draws_from_labels(frame, pca_components=None, umap_dim=None)
+
+
+def test_a_cell_can_be_selected_explicitly() -> None:
+    frame = pd.concat([_labels(), _labels(umap_dim=8)], ignore_index=True)
+
+    draws = cli.draws_from_labels(frame, pca_components=None, umap_dim=8)
+
+    assert len(draws) == 2
+
+
+def test_missing_row_identity_columns_is_an_explicit_error() -> None:
+    frame = _labels().drop(columns=["pmid", "itext", "a_abs", "b_abs"])
+
+    with pytest.raises(Exception, match="identify"):
+        cli.draws_from_labels(frame, pca_components=None, umap_dim=None)
+
+
+def test_end_to_end_stability_on_persisted_labels() -> None:
+    from pelinker.clustering.stability import analyze_stability
+
+    draws = cli.draws_from_labels(_labels(), pca_components=None, umap_dim=None)
+
+    summary = analyze_stability(draws).to_jsonable()
+
+    assert summary["mean_pair_jaccard"] == 1.0
+    assert summary["persistent_fraction"] == 1.0
diff --git a/test/test_cluster_viz.py b/test/test_cluster_viz.py
index dcbb8e2..fd4b159 100644
--- a/test/test_cluster_viz.py
+++ b/test/test_cluster_viz.py
@@ -5,7 +5,7 @@
 import numpy as np
 from sklearn.decomposition import PCA
 
-from pelinker.transform import EmbeddingTransformer, TransformConfig
+from pelinker.clustering.transform import EmbeddingTransformer, TransformConfig
 
 
 def test_cluster_viz_pca_matches_manual_projection() -> None:
diff --git a/test/test_clustering_fit_alignment.py b/test/test_clustering_fit_alignment.py
index 2329262..0b35379 100644
--- a/test/test_clustering_fit_alignment.py
+++ b/test/test_clustering_fit_alignment.py
@@ -7,9 +7,10 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.analysis import drop_entities_with_few_mentions, split_by_negative_label
-from pelinker.clustering_fit import fit_manifold_clustering
-from pelinker.config import (
+from pelinker.data.frames import drop_entities_with_few_mentions
+from pelinker.screener.evaluation import split_by_negative_label
+from pelinker.clustering.fit import fit_manifold_clustering
+from pelinker.core.config import (
     ClusteringOptimizationConfig,
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
@@ -18,9 +19,9 @@
     TransformConfig,
 )
 from pelinker.model import Linker
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.sampling import draw_selection_sample
-from pelinker.selection import load_selection_frame
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.search.sampling import draw_selection_sample
+from pelinker.search.selection import load_selection_frame
 
 _SUBSAMPLE_ROWS = 80
 
diff --git a/test/test_clustering_fusion_ranking.py b/test/test_clustering_fusion_ranking.py
index 0f3249f..8238578 100644
--- a/test/test_clustering_fusion_ranking.py
+++ b/test/test_clustering_fusion_ranking.py
@@ -4,7 +4,7 @@
 
 import pytest
 
-from pelinker.clustering_fusion_ranking import (
+from pelinker.clustering.fusion_ranking import (
     singleton_items_by_dbcv_score,
     top_k_fusion_candidates_by_dbcv_proxy,
 )
diff --git a/test/test_clustering_grid_solver.py b/test/test_clustering_grid_solver.py
index c0a2157..d14d34a 100644
--- a/test/test_clustering_grid_solver.py
+++ b/test/test_clustering_grid_solver.py
@@ -1,10 +1,10 @@
-"""Tests for typed grid aggregation and smoothed min_cluster_size selection."""
+"""Tests for typed grid aggregation and min_cluster_size selection."""
 
 import numpy as np
 import pandas as pd
 import pytest
 
-from pelinker.clustering_grid import (
+from pelinker.clustering.grid import (
     AggregatedGridPoint,
     AggregatedGridReport,
     ScalarMetricAggregate,
@@ -113,66 +113,91 @@ def test_solve_empty_report_raises() -> None:
         solve_optimal_min_cluster_size_from_aggregated(AggregatedGridReport(points=()))
 
 
-def test_solve_plateau_prefers_leftmost_high_flat_region() -> None:
-    """Sharp rise then flat top: derivative small on the right; pick early plateau x."""
-    sizes = [10, 15, 20, 25, 30, 35, 40]
-    means = [0.1, 0.2, 0.5, 1.5, 2.0, 2.01, 2.0]
-    stds = [0.05] * len(sizes)
-    counts = [10] * len(sizes)
-    r = _report_from_arrays(sizes, means, stds, counts)
-    out = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        method="mean",
-        smooth_window=3,
-        plateau_fraction=0.9,
-        derivative_rel_tol=0.2,
-        precision_weighted_smooth=False,
-    )
-    assert out.selection == "plateau_derivative"
-    assert out.chosen_min_cluster_size in {25, 30, 35}
-    assert out.score_mean_at_chosen == pytest.approx(
-        means[sizes.index(out.chosen_min_cluster_size)]
-    )
-
-
-def test_solve_smoothed_argmax_when_no_plateau() -> None:
-    """Strictly increasing objective: derivative stays positive → fall back to smoothed peak."""
+def test_solve_takes_argmax_when_nothing_ties() -> None:
+    """Well-separated peak with tight errors: the one-SE rule must not wander off it."""
     sizes = [10, 15, 20, 25, 30]
     means = [0.1, 0.4, 0.7, 1.0, 1.3]
     r = _report_from_arrays(sizes, means, [0.01] * 5, [5] * 5)
     out = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        method="mean",
-        smooth_window=3,
-        plateau_fraction=0.999,
-        derivative_rel_tol=1e-9,
-        precision_weighted_smooth=False,
+        r, objective="dbcv", smooth_window=1
     )
-    assert out.selection == "smoothed_argmax"
+    assert out.selection == "argmax"
     assert out.chosen_min_cluster_size == 30
+    assert out.argmax_min_cluster_size == 30
+
+
+def test_solve_slides_right_across_a_statistical_tie() -> None:
+    """Flat top within noise: prefer the largest tying min_cluster_size (parsimony)."""
+    sizes = [10, 15, 20, 25, 30, 35, 40]
+    means = [0.1, 0.2, 0.5, 1.5, 2.00, 2.01, 2.00]
+    r = _report_from_arrays(sizes, means, [0.30] * len(sizes), [10] * len(sizes))
+    out = solve_optimal_min_cluster_size_from_aggregated(
+        r, objective="dbcv", smooth_window=1, one_se_k=1.0
+    )
+    assert out.argmax_min_cluster_size == 35
+    assert out.chosen_min_cluster_size == 40
+    assert out.selection == "one_se_paired"
 
 
-def test_solve_lower_bound_objective() -> None:
+def test_one_se_never_promotes_a_worse_mean() -> None:
+    """The old ``mean - k*std`` discount let a low-variance, low-mean point win. It must not."""
     sizes = [10, 20]
     means = [0.2, 1.0]
-    stds = [0.05, 0.01]
+    stds = [0.05, 0.40]  # the *worse* point is far more precise
     r = _report_from_arrays(sizes, means, stds, [5, 5])
     out = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        method="lower_bound",
-        uncertainty_penalty=1.0,
-        smooth_window=1,
-        plateau_fraction=0.9,
-        derivative_rel_tol=1.0,
-        precision_weighted_smooth=False,
+        r, objective="dbcv", smooth_window=1
     )
     assert out.chosen_min_cluster_size == 20
 
 
-def test_solve_unknown_method_raises() -> None:
-    r = _report_from_arrays([10], [1.0], [0.0], [1])
-    with pytest.raises(ValueError, match="Unknown optimization method"):
-        solve_optimal_min_cluster_size_from_aggregated(r, method="nope")
+def test_one_se_k_zero_is_pure_argmax() -> None:
+    sizes = [10, 20, 30]
+    r = _report_from_arrays(sizes, [1.0, 2.0, 1.99], [0.5] * 3, [8] * 3)
+    tied = solve_optimal_min_cluster_size_from_aggregated(
+        r, objective="dbcv", smooth_window=1, one_se_k=1.0
+    )
+    strict = solve_optimal_min_cluster_size_from_aggregated(
+        r, objective="dbcv", smooth_window=1, one_se_k=0.0
+    )
+    assert tied.chosen_min_cluster_size == 30
+    assert strict.chosen_min_cluster_size == 20
+    assert strict.selection == "argmax"
+
+
+def test_contiguity_blocks_an_isolated_far_point() -> None:
+    """A wide-error point past a clearly worse one is not part of the plateau."""
+    sizes = [10, 20, 30, 40]
+    means = [1.0, 2.0, 0.1, 1.95]
+    stds = [0.05, 0.05, 0.05, 0.05]
+    r = _report_from_arrays(sizes, means, stds, [50] * 4)
+    contig = solve_optimal_min_cluster_size_from_aggregated(
+        r, objective="dbcv", smooth_window=1, one_se_k=5.0, one_se_contiguous=True
+    )
+    loose = solve_optimal_min_cluster_size_from_aggregated(
+        r, objective="dbcv", smooth_window=1, one_se_k=5.0, one_se_contiguous=False
+    )
+    assert contig.chosen_min_cluster_size == 20
+    assert loose.chosen_min_cluster_size == 40
+
+
+def test_one_se_rule_is_skipped_below_min_samples() -> None:
+    """An SE estimated from 3 numbers is mostly noise; acting on it hurts reproducibility."""
+    sizes = [10, 20, 30]
+    means = [1.0, 2.0, 1.99]
+    r = _report_from_arrays(sizes, means, [0.5] * 3, [3] * 3)
+    out = solve_optimal_min_cluster_size_from_aggregated(
+        r, objective="dbcv", smooth_window=1, one_se_k=1.0, one_se_min_samples=6
+    )
+    assert out.chosen_min_cluster_size == 20
+    assert out.selection == "argmax"
+    assert out.one_se_k == 0.0  # records the k actually applied
+
+    enough = _report_from_arrays(sizes, means, [0.5] * 3, [8] * 3)
+    slid = solve_optimal_min_cluster_size_from_aggregated(
+        enough, objective="dbcv", smooth_window=1, one_se_k=1.0, one_se_min_samples=6
+    )
+    assert slid.chosen_min_cluster_size == 30
 
 
 def test_solve_unknown_objective_raises() -> None:
@@ -184,88 +209,26 @@ def test_solve_unknown_objective_raises() -> None:
         )
 
 
-def test_user_style_noisy_sequence_reasonable_choice() -> None:
-    """Noisy scores that rise then wiggle near a ceiling (similar to user example)."""
-    sizes = list(range(10, 10 + 8 * 5, 5))
-    means = [0.9, 0.8, 1.6, 2.2, 2.4, 2.3, 2.5, 2.6]
-    stds = [0.15] * len(sizes)
-    counts = [8] * len(sizes)
-    r = _report_from_arrays(sizes, means, stds, counts)
-    out = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        method="mean",
-        smooth_window=3,
-        plateau_fraction=0.88,
-        derivative_rel_tol=0.25,
-        precision_weighted_smooth=True,
-    )
-    assert out.chosen_min_cluster_size in set(sizes)
-    assert out.score_mean_at_chosen in means
-    assert len(out.dy_dx) == len(out.x)
-
-
-def test_noisy_dbcv_does_not_pick_spurious_early_plateau() -> None:
-    """Regression: fractional ``plateau_fraction * y_max`` used to admit mediocre early points."""
-    sizes = [10, 15, 20, 25, 30, 35, 40, 45, 50, 55]
-    means = [
-        0.399424,
-        0.379638,
-        0.412020,
-        0.369532,
-        0.414304,
-        0.389333,
-        0.400889,
-        0.402151,
-        0.396124,
-        0.464974,
-    ]
-    r = _report_from_arrays(sizes, means, [0.02] * len(sizes), [5] * len(sizes))
-    out = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        objective="dbcv",
-        method="mean",
-        smooth_window=3,
-        plateau_fraction=0.92,
-        derivative_rel_tol=0.12,
-        precision_weighted_smooth=False,
-    )
-    assert out.chosen_min_cluster_size == 55
-
-
 def test_cluster_count_reward_prefers_more_clusters_on_flat_dbcv() -> None:
-    """When DBCV is flat, log cluster penalty should favor smaller min_cluster_size (more clusters)."""
+    """When DBCV is flat, the log cluster reward should favour smaller min_cluster_size."""
     sizes = [20, 40, 60, 80]
     means = [0.70, 0.71, 0.69, 0.70]
     n_clusters = [120.0, 80.0, 60.0, 50.0]
     r = _report_from_arrays(sizes, means, [0.02] * 4, [5] * 4, n_clusters=n_clusters)
     without = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        objective="dbcv",
-        method="mean",
-        smooth_window=1,
-        plateau_fraction=0.5,
-        derivative_rel_tol=1.0,
-        precision_weighted_smooth=False,
-        cluster_count_reward=0.0,
+        r, objective="dbcv", smooth_window=1, cluster_count_reward=0.0
     )
     with_reward = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        objective="dbcv",
-        method="mean",
-        smooth_window=1,
-        plateau_fraction=0.5,
-        derivative_rel_tol=1.0,
-        precision_weighted_smooth=False,
-        cluster_count_reward=0.15,
+        r, objective="dbcv", smooth_window=1, cluster_count_reward=0.15
     )
     assert without.chosen_min_cluster_size >= with_reward.chosen_min_cluster_size
-    assert with_reward.chosen_min_cluster_size == 20
+    assert with_reward.argmax_min_cluster_size == 20
     assert with_reward.y_cluster_term[0] == pytest.approx(0.0)
     assert all(t <= 0.0 for t in with_reward.y_cluster_term)
 
 
 def test_pooled_grid_solve_from_metrics_dfs_returns_y_objective() -> None:
-    from pelinker.analysis import pooled_grid_solve_from_metrics_dfs
+    from pelinker.search.grid_solver import pooled_grid_solve_from_metrics_dfs
 
     sizes = [20, 40, 60]
     df = pd.DataFrame(
@@ -277,11 +240,10 @@ def test_pooled_grid_solve_from_metrics_dfs_returns_y_objective() -> None:
             "ari": [0.9, 0.91, 0.92],
         }
     )
-    solved = pooled_grid_solve_from_metrics_dfs(
-        [df],
-        optimization_config=None,
-    )
+    solved = pooled_grid_solve_from_metrics_dfs([df], optimization_config=None)
     assert len(solved.y_objective) == len(solved.x)
+    assert len(solved.y_se) == len(solved.x)
+    assert len(solved.y_eligible) == len(solved.x)
     assert solved.chosen_min_cluster_size in sizes
 
 
@@ -296,10 +258,6 @@ def test_finite_mask_drops_non_finite_objective() -> None:
     means = [float("nan"), 1.0, 2.0]
     r = _report_from_arrays(sizes, means, [0.0, 0.0, 0.0], [1, 1, 1])
     out = solve_optimal_min_cluster_size_from_aggregated(
-        r,
-        smooth_window=1,
-        plateau_fraction=0.5,
-        derivative_rel_tol=1.0,
-        precision_weighted_smooth=False,
+        r, objective="dbcv", smooth_window=1
     )
     assert out.x == (15.0, 20.0)
diff --git a/test/test_clustering_quality_checkpoint.py b/test/test_clustering_quality_checkpoint.py
index 3b9d1d5..8542165 100644
--- a/test/test_clustering_quality_checkpoint.py
+++ b/test/test_clustering_quality_checkpoint.py
@@ -7,7 +7,7 @@
 
 import pytest
 
-from pelinker.model_selection_checkpoint import (
+from pelinker.search.model_selection_checkpoint import (
     CHECKPOINT_VERSION,
     ModelSelectionCheckpoint,
     combination_key_from_members,
@@ -18,12 +18,12 @@
     reconcile_fusion_checkpoint_params,
     save_checkpoint_atomic,
 )
-from pelinker.reporting import (
+from pelinker.reports.schema import (
     ClusteringSearchSummaryRow,
     HyperparameterSearchStats,
     MeanWithUncertainty,
-    clustering_search_summary_row_from_flat_dict,
 )
+from pelinker.reports.summary import clustering_search_summary_row_from_flat_dict
 
 
 def test_combination_key_sorted_stable() -> None:
diff --git a/test/test_clustering_quality_fine_metadata.py b/test/test_clustering_quality_fine_metadata.py
index de93c9b..9dbf7d3 100644
--- a/test/test_clustering_quality_fine_metadata.py
+++ b/test/test_clustering_quality_fine_metadata.py
@@ -6,13 +6,10 @@
 import pandas as pd
 import pytest
 
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.reporting import (
-    ClusteringHyperparameters,
-    ModelSelectionReport,
-    entity_negative_label_mask_01,
-)
-from pelinker.model_selection.fine_metadata import (
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.data.frames import entity_negative_label_mask_01
+from pelinker.reports.schema import ClusteringHyperparameters, ModelSelectionReport
+from pelinker.search.model_selection.fine_metadata import (
     clustering_metadata_df,
     validated_oov_label_series,
 )
diff --git a/test/test_clustering_search_ranking.py b/test/test_clustering_search_ranking.py
index 7c9f14a..62e72d4 100644
--- a/test/test_clustering_search_ranking.py
+++ b/test/test_clustering_search_ranking.py
@@ -5,32 +5,38 @@
 import pandas as pd
 import pytest
 
-from pelinker.clustering_search_ranking import (
+from pelinker.clustering.ranking import (
     OUTER_SCORE_COL,
     attach_outer_scores,
     outer_score_from_summary_row,
     pick_best_row,
     raw_outer_dbcv_ari_score,
 )
-from pelinker.reporting import (
+from pelinker.reports.schema import (
     ClusteringSearchSummaryRow,
     HyperparameterSearchStats,
     MeanWithUncertainty,
 )
 
 
-def test_raw_outer_averages_when_both_finite() -> None:
+def test_raw_outer_is_the_clipped_geomean() -> None:
     score, std = raw_outer_dbcv_ari_score(0.8, 0.4, dbcv_std=0.0, ari_std=0.0)
-    assert score == pytest.approx(0.6)
+    assert score == pytest.approx((0.8 * 0.4) ** 0.5)
     assert std == 0.0
 
 
+def test_raw_outer_clips_negative_dbcv_to_zero() -> None:
+    score, _ = raw_outer_dbcv_ari_score(-0.2, 0.5)
+    assert score == 0.0
+
+
 def test_raw_outer_falls_back_to_dbcv() -> None:
     score, _ = raw_outer_dbcv_ari_score(0.7, None)
     assert score == 0.7
 
 
-def test_attach_outer_minmax_ranks_joint() -> None:
+def test_attach_outer_breaks_the_tie_minmax_used_to_manufacture() -> None:
+    """Per-curve min-max mapped both rows to (1,0) and (0,1), scoring both 0.5."""
     df = pd.DataFrame(
         {
             "best_score": [0.9, 0.5],
@@ -41,9 +47,41 @@ def test_attach_outer_minmax_ranks_joint() -> None:
             "layer": ["1", "1"],
         }
     )
-    scored = attach_outer_scores(df, use_minmax=True)
-    # After minmax: a=(1,0)→0.5; b=(0,1)→0.5 — tie on outer.
-    assert scored[OUTER_SCORE_COL].tolist() == pytest.approx([0.5, 0.5])
+    scored = attach_outer_scores(df)
+    assert scored[OUTER_SCORE_COL].tolist() == pytest.approx(
+        [(0.9 * 0.1) ** 0.5, (0.5 * 0.9) ** 0.5]
+    )
+    # b is better on both metrics jointly; the geometric mean says so.
+    assert scored[OUTER_SCORE_COL][1] > scored[OUTER_SCORE_COL][0]
+
+
+def test_outer_scores_do_not_depend_on_other_candidates() -> None:
+    """Min-max across the leaderboard let an irrelevant row reorder the rest."""
+    base = pd.DataFrame(
+        {
+            "best_score": [0.6, 0.4],
+            "best_score_std": [0.0, 0.0],
+            "ari": [0.3, 0.5],
+            "ari_std": [0.0, 0.0],
+        }
+    )
+    extra = pd.concat(
+        [
+            base,
+            pd.DataFrame(
+                {
+                    "best_score": [0.01],
+                    "best_score_std": [0.0],
+                    "ari": [0.99],
+                    "ari_std": [0.0],
+                }
+            ),
+        ],
+        ignore_index=True,
+    )
+    assert attach_outer_scores(base)[OUTER_SCORE_COL].tolist() == pytest.approx(
+        attach_outer_scores(extra)[OUTER_SCORE_COL].tolist()[:2]
+    )
 
 
 def test_pick_best_row_breaks_ties_by_model() -> None:
@@ -57,7 +95,7 @@ def test_pick_best_row_breaks_ties_by_model() -> None:
             "layer": ["1", "1"],
         }
     )
-    winner = pick_best_row(df, tie_break_cols=("model", "layer"), use_minmax=True)
+    winner = pick_best_row(df, tie_break_cols=("model", "layer"))
     assert winner["model"] == "a"
 
 
@@ -73,4 +111,4 @@ def test_outer_score_from_summary_row() -> None:
         dbcv=MeanWithUncertainty(mean=0.8, std=0.0),
         ari=MeanWithUncertainty(mean=0.4, std=0.0),
     )
-    assert outer_score_from_summary_row(row) == pytest.approx(0.6)
+    assert outer_score_from_summary_row(row) == pytest.approx((0.8 * 0.4) ** 0.5)
diff --git a/test/test_config.py b/test/test_config.py
index c7b8da8..a9d8085 100644
--- a/test/test_config.py
+++ b/test/test_config.py
@@ -4,7 +4,7 @@
 
 import pytest
 
-from pelinker.config import ClusteringOptimizationConfig, KBConfig
+from pelinker.core.config import ClusteringOptimizationConfig, KBConfig
 
 
 def test_kb_config_ok():
@@ -88,7 +88,7 @@ def test_clustering_optimization_rejects_invalid_grid_n_entities() -> None:
 
 
 def test_linker_fit_config_load_fields_validate() -> None:
-    from pelinker.config import LinkerFitConfig
+    from pelinker.core.config import LinkerFitConfig
 
     LinkerFitConfig(
         drop_rare_entities=True,
@@ -99,14 +99,14 @@ def test_linker_fit_config_load_fields_validate() -> None:
 
 
 def test_linker_fit_config_rejects_invalid_max_mentions() -> None:
-    from pelinker.config import LinkerFitConfig
+    from pelinker.core.config import LinkerFitConfig
 
     with pytest.raises(ValueError, match="max_mentions_per_entity"):
         LinkerFitConfig(max_mentions_per_entity=0)
 
 
 def test_fingerprint_includes_mention_load_fields(tmp_path: pathlib.Path) -> None:
-    from pelinker.model_selection_checkpoint import fingerprint_config_from_cli
+    from pelinker.search.model_selection_checkpoint import fingerprint_config_from_cli
 
     d = tmp_path / "in"
     d.mkdir()
diff --git a/test/test_corpus_stream.py b/test/test_corpus_stream.py
new file mode 100644
index 0000000..74e64eb
--- /dev/null
+++ b/test/test_corpus_stream.py
@@ -0,0 +1,21 @@
+"""The embedding corpus stream re-attaches the peeked first chunk without self-reference."""
+
+from __future__ import annotations
+
+from pelinker.embed.corpus import prepend_first
+
+
+def test_prepend_first_yields_every_chunk_once() -> None:
+    source = iter(["a", "b", "c"])
+    first = next(source)
+
+    assert list(prepend_first(first, source)) == ["a", "b", "c"]
+
+
+def test_rebinding_the_source_name_does_not_make_the_stream_consume_itself() -> None:
+    """The bug this guards: a closure over a rebound name raised at the second chunk."""
+    chunks = iter(range(4))
+    first = next(chunks)
+    chunks = prepend_first(first, chunks)
+
+    assert list(chunks) == [0, 1, 2, 3]
diff --git a/test/test_derive_inverse_pairs.py b/test/test_derive_inverse_pairs.py
new file mode 100644
index 0000000..a7c5965
--- /dev/null
+++ b/test/test_derive_inverse_pairs.py
@@ -0,0 +1,238 @@
+"""Curator review of derived converse pairs: accept, reject, and what stays pending."""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+_MODULE_PATH = (
+    Path(__file__).resolve().parents[1]
+    / "run"
+    / "preprocessing"
+    / "derive_inverse_pairs.py"
+)
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("derive_inverse_pairs", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+dip = _load_module()
+
+
+@pytest.fixture
+def paired() -> pd.DataFrame:
+    """Two derived pairs plus one unpaired entry, as ``derive_pairs`` would emit them."""
+    return pd.DataFrame(
+        {
+            "entity_id": ["PEL.1", "RO.2", "PEL.3", "PEL.4", "PEL.9"],
+            "label": [
+                "regulates",
+                "regulated by",
+                "expresses",
+                "expressed in",
+                "secretes",
+            ],
+            "inverse_entity_id": ["RO.2", "PEL.1", "PEL.4", "PEL.3", None],
+            "inverse_label": [
+                "regulated by",
+                "regulates",
+                "expressed in",
+                "expresses",
+                None,
+            ],
+            "inverse_source": [
+                "passive_by",
+                "passive_by",
+                "passive_prep",
+                "passive_prep",
+                None,
+            ],
+            "needs_review": [True, True, True, True, False],
+            "canonical_entity_id": ["PEL.1", "PEL.1", "PEL.3", "PEL.3", "PEL.9"],
+            "is_canonical": [True, False, True, False, True],
+            "description": ["", "", "", "", ""],
+        }
+    )
+
+
+def _verdicts(**pairs: str) -> dict[frozenset[str], str]:
+    return {frozenset(k.split("__")): v for k, v in pairs.items()}
+
+
+def test_accepting_a_pair_only_clears_the_review_flag(paired) -> None:
+    reviewed = dip.apply_review(paired, _verdicts(**{"PEL.1__RO.2": "accept"}))
+
+    row = reviewed.loc[reviewed["entity_id"] == "RO.2"].iloc[0]
+    assert not bool(row["needs_review"])
+    # The pair itself is untouched: the curator confirmed it, so the converse member still
+    # folds onto the canonical id.
+    assert row["inverse_entity_id"] == "PEL.1"
+    assert row["canonical_entity_id"] == "PEL.1"
+
+
+def test_rejecting_a_pair_unlinks_both_members(paired) -> None:
+    """A rejected pair must stop collapsing two relations onto one canonical id."""
+    reviewed = dip.apply_review(paired, _verdicts(**{"PEL.3__PEL.4": "reject"}))
+
+    for entity_id in ("PEL.3", "PEL.4"):
+        row = reviewed.loc[reviewed["entity_id"] == entity_id].iloc[0]
+        assert pd.isna(row["inverse_entity_id"]) or row["inverse_entity_id"] is None
+        assert not bool(row["needs_review"])
+        assert row["canonical_entity_id"] == entity_id
+        assert bool(row["is_canonical"])
+
+
+def test_an_untouched_pair_is_unaffected_by_another_verdict(paired) -> None:
+    reviewed = dip.apply_review(paired, _verdicts(**{"PEL.3__PEL.4": "reject"}))
+
+    row = reviewed.loc[reviewed["entity_id"] == "RO.2"].iloc[0]
+    assert row["inverse_entity_id"] == "PEL.1"
+    assert bool(row["needs_review"])
+
+
+def test_pending_sheet_has_one_row_per_pair_not_per_member(paired) -> None:
+    pending = dip.pending_review_sheet(paired)
+
+    assert len(pending) == 2
+    assert set(pending["verdict"]) == {""}
+    assert {
+        frozenset(p) for p in zip(pending["entity_id"], pending["inverse_entity_id"])
+    } == {
+        frozenset({"PEL.1", "RO.2"}),
+        frozenset({"PEL.3", "PEL.4"}),
+    }
+
+
+def test_reviewed_pairs_leave_the_pending_sheet(paired) -> None:
+    reviewed = dip.apply_review(
+        paired, _verdicts(**{"PEL.1__RO.2": "accept", "PEL.3__PEL.4": "reject"})
+    )
+
+    assert len(dip.pending_review_sheet(reviewed)) == 0
+
+
+def test_load_review_rejects_an_unknown_verdict(tmp_path: Path) -> None:
+    sheet = tmp_path / "review.csv"
+    sheet.write_text(
+        "entity_id,inverse_entity_id,verdict,note\nPEL.1,RO.2,maybe,\n",
+        encoding="utf-8",
+    )
+
+    with pytest.raises(Exception, match="verdict"):
+        dip.load_review(sheet)
+
+
+def test_load_review_skips_rows_the_curator_has_not_decided(tmp_path: Path) -> None:
+    """An unfilled row is 'not yet reviewed', not an error — sheets arrive half-done."""
+    sheet = tmp_path / "review.csv"
+    sheet.write_text(
+        "entity_id,inverse_entity_id,verdict,note\nPEL.1,RO.2,accept,\nPEL.3,PEL.4,,\n",
+        encoding="utf-8",
+    )
+
+    verdicts = dip.load_review(sheet)
+
+    assert verdicts == {frozenset({"PEL.1", "RO.2"}): "accept"}
+
+
+def test_load_review_requires_the_expected_columns(tmp_path: Path) -> None:
+    sheet = tmp_path / "review.csv"
+    sheet.write_text("entity_id,verdict\nPEL.1,accept\n", encoding="utf-8")
+
+    with pytest.raises(Exception, match="inverse_entity_id"):
+        dip.load_review(sheet)
+
+
+# ------------------------------------------------------------------ symmetry
+
+
+def test_symmetry_is_joined_from_the_ro_extraction() -> None:
+    """The curated KB carries no RO axioms, so the flag comes from the RO table by id."""
+    kb = pd.DataFrame({"entity_id": ["RO.1", "PEL.2"], "label": ["overlaps", "binds"]})
+    ro = pd.DataFrame({"entity_id": ["RO.1", "RO.9"], "is_symmetric": [True, True]})
+
+    out = dip.attach_symmetry(kb, ro)
+
+    assert out.set_index("entity_id")["is_symmetric"].to_dict() == {
+        "RO.1": True,
+        "PEL.2": False,
+    }
+
+
+def test_a_kb_that_already_flags_symmetry_is_not_overridden() -> None:
+    kb = pd.DataFrame({"entity_id": ["RO.1"], "label": ["x"], "is_symmetric": [False]})
+    ro = pd.DataFrame({"entity_id": ["RO.1"], "is_symmetric": [True]})
+
+    out = dip.attach_symmetry(kb, ro)
+
+    assert not bool(out["is_symmetric"].iloc[0])
+
+
+def test_without_an_ro_table_nothing_is_symmetric() -> None:
+    kb = pd.DataFrame({"entity_id": ["RO.1"], "label": ["overlaps"]})
+
+    assert not dip.attach_symmetry(kb, None)["is_symmetric"].any()
+
+
+def test_a_symmetric_relation_is_never_paired_by_a_surface_rule(nlp) -> None:
+    """ "connected to" looks passive, but it has no converse: pairing it with "connects"
+    would fold two distinct relations onto one canonical id."""
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["RO.70", "RO.76", "PEL.1", "RO.2"],
+            "label": ["connected to", "connects", "regulates", "regulated by"],
+            "is_symmetric": [True, False, False, False],
+        }
+    )
+
+    paired = dip.derive_pairs(kb, nlp).set_index("entity_id")
+
+    assert pd.isna(paired.loc["RO.70", "inverse_entity_id"])
+    assert pd.isna(paired.loc["RO.76", "inverse_entity_id"])
+    assert bool(paired.loc["RO.70", "is_canonical"])
+    # The non-symmetric pair next to it is still derived.
+    assert paired.loc["RO.2", "canonical_entity_id"] == "PEL.1"
+
+
+def test_irregular_participles_count_as_passive() -> None:
+    """ "bound by" ↔ "binds to": the participle ends in neither -ed nor -en."""
+    assert dip.is_passive_form("bound by")
+    assert dip.is_passive_form("regulated by")
+    assert not dip.is_passive_form("binds to")
+    assert not dip.is_passive_form("bound")
+
+
+def test_curated_ids_are_symmetric_on_top_of_ro() -> None:
+    """RO does not cover GeneWays verbs; a curator can still mark "binds to" symmetric."""
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["RO.1", "PEL.3", "PEL.4"],
+            "label": ["overlaps", "binds to", "x"],
+        }
+    )
+    ro = pd.DataFrame({"entity_id": ["RO.1"], "is_symmetric": [True]})
+
+    out = dip.attach_symmetry(kb, ro, frozenset({"PEL.3"}))
+
+    assert out.set_index("entity_id")["is_symmetric"].to_dict() == {
+        "RO.1": True,
+        "PEL.3": True,
+        "PEL.4": False,
+    }
+
+
+def test_load_symmetric_reads_entity_ids(tmp_path: Path) -> None:
+    sheet = tmp_path / "symmetric.csv"
+    sheet.write_text("entity_id,label,note\nPEL.3,binds to,\n,,\n", encoding="utf-8")
+
+    assert dip.load_symmetric(sheet) == frozenset({"PEL.3"})
diff --git a/test/test_dim_selection_checkpoint.py b/test/test_dim_selection_checkpoint.py
index 7b90f57..dd19f20 100644
--- a/test/test_dim_selection_checkpoint.py
+++ b/test/test_dim_selection_checkpoint.py
@@ -7,7 +7,7 @@
 
 import pytest
 
-from pelinker.dim_selection.checkpoint import (
+from pelinker.search.dim_selection.checkpoint import (
     CHECKPOINT_VERSION,
     DimSelectionCheckpoint,
     compute_run_fingerprint,
@@ -17,7 +17,7 @@
     new_checkpoint,
     save_checkpoint_atomic,
 )
-from pelinker.dim_selection.grids import cell_key
+from pelinker.search.dim_selection.grids import cell_key
 
 
 def test_checkpoint_save_load_roundtrip(tmp_path: pathlib.Path) -> None:
diff --git a/test/test_dim_selection_grids.py b/test/test_dim_selection_grids.py
index 4e23f52..50ac87e 100644
--- a/test/test_dim_selection_grids.py
+++ b/test/test_dim_selection_grids.py
@@ -5,7 +5,7 @@
 import pandas as pd
 import pytest
 
-from pelinker.dim_selection.grids import (
+from pelinker.search.dim_selection.grids import (
     cell_key,
     cluster_viz_components_for_umap,
     coarse_cells,
diff --git a/test/test_dim_selection_runner.py b/test/test_dim_selection_runner.py
index 483705b..55a1551 100644
--- a/test/test_dim_selection_runner.py
+++ b/test/test_dim_selection_runner.py
@@ -8,18 +8,15 @@
 import pandas as pd
 import pytest
 
-from pelinker.dim_selection.grids import cell_key
-from pelinker.dim_selection.runner import run_dim_selection
-from pelinker.dim_selection.summary import (
+from pelinker.search.dim_selection.grids import cell_key
+from pelinker.search.dim_selection.runner import run_dim_selection
+from pelinker.search.dim_selection.summary import (
     DIM_SELECTION_RESULTS_CSV_BASENAME,
     DIM_SELECTION_SUMMARY_JSON_BASENAME,
 )
-from pelinker.reporting import (
-    ClusteringHyperparameters,
-    ModelSelectionReport,
-    entity_negative_label_mask_01,
-)
-from pelinker.transform import TransformConfig
+from pelinker.data.frames import entity_negative_label_mask_01
+from pelinker.reports.schema import ClusteringHyperparameters, ModelSelectionReport
+from pelinker.clustering.transform import TransformConfig
 
 
 def _metrics_for_score(dbcv: float, ari: float = 0.7) -> pd.DataFrame:
@@ -80,35 +77,35 @@ def test_run_dim_selection_coarse_then_refine(
     base = pd.DataFrame({"entity": ["a"], "embed": [np.zeros(4)]})
 
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.load_selection_frame",
+        "pelinker.search.dim_selection.runner.load_selection_frame",
         lambda **_kwargs: base,
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.draw_selection_sample",
+        "pelinker.search.dim_selection.runner.draw_selection_sample",
         lambda frame, _cfg, sample_index: frame,
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.plot_metrics", lambda *_a, **_k: None
+        "pelinker.search.dim_selection.runner.plot_metrics", lambda *_a, **_k: None
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.plot_metrics_with_error_bars",
+        "pelinker.search.dim_selection.runner.plot_metrics_with_error_bars",
         lambda *_a, **_k: None,
     )
     # Avoid matplotlib write noise for summary figures in CI.
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_heatmaps",
+        "pelinker.search.dim_selection.summary.write_dim_heatmaps",
         lambda *_a, **_k: [],
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_outer_surface",
+        "pelinker.search.dim_selection.summary.write_dim_outer_surface",
         lambda *_a, **_k: [],
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_metrics_violin",
+        "pelinker.search.dim_selection.summary.write_dim_metrics_violin",
         lambda *_a, **_k: [],
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_dbcv_vs_ari",
+        "pelinker.search.dim_selection.summary.write_dim_dbcv_vs_ari",
         lambda *_a, **_k: [],
     )
 
@@ -124,7 +121,7 @@ def tracking_evaluate(frame, transform_config, **kwargs):
         return _fake_evaluate(frame, transform_config, **kwargs)
 
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.evaluate_selection_sample",
+        "pelinker.search.dim_selection.runner.evaluate_selection_sample",
         tracking_evaluate,
     )
 
@@ -187,34 +184,34 @@ def test_fingerprint_mismatch_aborts(
     base = pd.DataFrame({"entity": ["a"], "embed": [np.zeros(4)]})
 
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.load_selection_frame",
+        "pelinker.search.dim_selection.runner.load_selection_frame",
         lambda **_kwargs: base,
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.draw_selection_sample",
+        "pelinker.search.dim_selection.runner.draw_selection_sample",
         lambda frame, _cfg, sample_index: frame,
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.evaluate_selection_sample",
+        "pelinker.search.dim_selection.runner.evaluate_selection_sample",
         _fake_evaluate,
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.runner.plot_metrics", lambda *_a, **_k: None
+        "pelinker.search.dim_selection.runner.plot_metrics", lambda *_a, **_k: None
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_heatmaps",
+        "pelinker.search.dim_selection.summary.write_dim_heatmaps",
         lambda *_a, **_k: [],
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_outer_surface",
+        "pelinker.search.dim_selection.summary.write_dim_outer_surface",
         lambda *_a, **_k: [],
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_metrics_violin",
+        "pelinker.search.dim_selection.summary.write_dim_metrics_violin",
         lambda *_a, **_k: [],
     )
     monkeypatch.setattr(
-        "pelinker.dim_selection.summary.write_dim_dbcv_vs_ari",
+        "pelinker.search.dim_selection.summary.write_dim_dbcv_vs_ari",
         lambda *_a, **_k: [],
     )
 
diff --git a/test/test_dim_selection_summary.py b/test/test_dim_selection_summary.py
index 9582869..a2ecbd9 100644
--- a/test/test_dim_selection_summary.py
+++ b/test/test_dim_selection_summary.py
@@ -7,7 +7,7 @@
 
 import pandas as pd
 
-from pelinker.dim_selection.summary import (
+from pelinker.search.dim_selection.summary import (
     DIM_SELECTION_ARI_HEATMAP_STEM,
     DIM_SELECTION_DBCV_HEATMAP_STEM,
     DIM_SELECTION_DBCV_VS_ARI_STEM,
diff --git a/test/test_direction_diagnostic.py b/test/test_direction_diagnostic.py
new file mode 100644
index 0000000..b4222bb
--- /dev/null
+++ b/test/test_direction_diagnostic.py
@@ -0,0 +1,118 @@
+"""Converse-pair collision diagnostic (run/analysis/direction_diagnostic.py)."""
+
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+_MODULE_PATH = (
+    Path(__file__).resolve().parents[1] / "run" / "analysis" / "direction_diagnostic.py"
+)
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("direction_diagnostic", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+diag = _load_module()
+
+
+@pytest.fixture
+def kb() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "entity_id": ["RO.1", "RO.2", "RO.3", "RO.4", "RO.5"],
+            "label": ["activates", "activated by", "part of", "has part", None],
+            "inverse_entity_id": ["RO.2", "RO.1", "RO.4", "RO.3", "RO.1"],
+        }
+    )
+
+
+def test_inverse_pairs_are_unordered_and_deduplicated(kb) -> None:
+    pairs = diag.load_inverse_pairs(kb)
+
+    # Each pair once, smaller id first; the unlabelled RO.5 row contributes nothing.
+    assert pairs == [("RO.1", "RO.2"), ("RO.3", "RO.4")]
+
+
+def test_missing_inverse_column_is_an_explicit_error() -> None:
+    with pytest.raises(Exception, match="inverse_entity_id"):
+        diag.load_inverse_pairs(pd.DataFrame({"entity_id": ["x"], "label": ["y"]}))
+
+
+def test_colliding_pair_shares_its_top_cluster(kb) -> None:
+    assignments = pd.DataFrame(
+        {
+            "entity": ["activates"] * 4 + ["activated by"] * 4,
+            "cluster": [1, 1, 1, 2, 1, 1, 1, 2],
+        }
+    )
+
+    rows, summary = diag.diagnose(
+        assignments,
+        [("RO.1", "RO.2")],
+        {"RO.1": "activates", "RO.2": "activated by"},
+        collision_threshold=0.5,
+    )
+
+    assert rows[0]["same_top_cluster"] is True
+    assert rows[0]["collides"] is True
+    assert rows[0]["mass_overlap"] == pytest.approx(1.0)
+    assert summary["collision_rate"] == 1.0
+    assert summary["disjoint_rate"] == 0.0
+
+
+def test_separated_pair_is_disjoint(kb) -> None:
+    assignments = pd.DataFrame(
+        {
+            "entity": ["part of"] * 3 + ["has part"] * 3,
+            "cluster": [5, 5, 5, 7, 7, 7],
+        }
+    )
+
+    rows, summary = diag.diagnose(
+        assignments,
+        [("RO.3", "RO.4")],
+        {"RO.3": "part of", "RO.4": "has part"},
+        collision_threshold=0.5,
+    )
+
+    assert rows[0]["disjoint"] is True
+    assert rows[0]["collides"] is False
+    assert summary["collision_rate"] == 0.0
+    assert summary["disjoint_rate"] == 1.0
+
+
+def test_pairs_absent_from_the_corpus_are_not_counted(kb) -> None:
+    assignments = pd.DataFrame({"entity": ["activates"] * 2, "cluster": [1, 1]})
+
+    rows, summary = diag.diagnose(
+        assignments,
+        [("RO.1", "RO.2")],
+        {"RO.1": "activates", "RO.2": "activated by"},
+        collision_threshold=0.5,
+    )
+
+    # One member never appears, so the pair is unobservable rather than separated.
+    assert rows == []
+    assert summary["n_pairs_observed"] == 0
+    assert summary["collision_rate"] is None
+
+
+def test_noise_rows_are_excluded_before_diagnosis() -> None:
+    from pelinker.clustering.composition import filter_emergent_assignments
+
+    assignments = pd.DataFrame(
+        {"entity": ["activates", "activates"], "cluster": [-1, 3]}
+    )
+
+    kept = filter_emergent_assignments(assignments)
+
+    assert list(kept["cluster"]) == [3]
diff --git a/test/test_distillation.py b/test/test_distillation.py
index 336a314..77e06c4 100644
--- a/test/test_distillation.py
+++ b/test/test_distillation.py
@@ -8,8 +8,8 @@
 import pandas as pd
 import pytest
 
-from pelinker.config import DistillationGateConfig
-from pelinker.distillation import (
+from pelinker.core.config import DistillationGateConfig
+from pelinker.linker.distillation import (
     DistillationFidelityMetrics,
     apply_gates,
     cluster_to_entity_map,
@@ -17,7 +17,7 @@
     evaluate_gates,
     grouped_holdout_split,
 )
-from pelinker.entity_head import fit_mlp_entity_head
+from pelinker.linker.entity_head import fit_mlp_entity_head
 
 NEG = "__NEGATIVE__"
 
@@ -83,7 +83,7 @@ def test_single_group_falls_back_to_row_split_with_a_warning(caplog) -> None:
     """One pmid cannot be split by group; the fallback must be reported, not silent."""
     frame = _frame(n_groups=1, per_group=20)
 
-    with caplog.at_level(logging.WARNING, logger="pelinker.distillation"):
+    with caplog.at_level(logging.WARNING, logger="pelinker.linker.distillation"):
         split = grouped_holdout_split(frame, holdout_fraction=0.2, random_state=0)
 
     assert split.grouping == "row"
@@ -95,7 +95,7 @@ def test_single_group_falls_back_to_row_split_with_a_warning(caplog) -> None:
 def test_missing_group_column_falls_back_to_row_split_with_a_warning(caplog) -> None:
     frame = pd.DataFrame({"entity": [f"e{i % 3}" for i in range(20)]})
 
-    with caplog.at_level(logging.WARNING, logger="pelinker.distillation"):
+    with caplog.at_level(logging.WARNING, logger="pelinker.linker.distillation"):
         split = grouped_holdout_split(frame, holdout_fraction=0.2, random_state=0)
 
     assert split.grouping == "row"
@@ -336,7 +336,7 @@ def test_an_unmeasurable_agreement_gate_passes_and_says_why() -> None:
 
 
 def test_apply_gates_warns_by_default_and_keeps_going(caplog) -> None:
-    with caplog.at_level(logging.WARNING, logger="pelinker.distillation"):
+    with caplog.at_level(logging.WARNING, logger="pelinker.linker.distillation"):
         results = apply_gates(
             _metrics_with(entity_agreement=0.50), DistillationGateConfig()
         )
diff --git a/test/test_distillation_fit_integration.py b/test/test_distillation_fit_integration.py
index 51d8753..390f29b 100644
--- a/test/test_distillation_fit_integration.py
+++ b/test/test_distillation_fit_integration.py
@@ -9,7 +9,7 @@
 import pandas as pd
 import pytest
 
-from pelinker.config import (
+from pelinker.core.config import (
     DistillationGateConfig,
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
@@ -18,7 +18,7 @@
     TransformConfig,
 )
 from pelinker.model import Linker
-from pelinker.reporting import clustering_report_to_jsonable_dict
+from pelinker.reports.io import clustering_report_to_jsonable_dict
 
 
 def _mentions_parquet(tmp_path: Path, n_ent: int = 24) -> tuple[Path, dict[str, str]]:
diff --git a/test/test_embedding_fusion.py b/test/test_embedding_fusion.py
index edf09c4..fdaea16 100644
--- a/test/test_embedding_fusion.py
+++ b/test/test_embedding_fusion.py
@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.embedding_fusion import (
+from pelinker.data.fusion import (
     JOIN_KEYS,
     dedupe_mean_embed_by_keys,
     fused_property_vectors_from_paths,
diff --git a/test/test_entity_head_compact.py b/test/test_entity_head_compact.py
index d613c92..0341945 100644
--- a/test/test_entity_head_compact.py
+++ b/test/test_entity_head_compact.py
@@ -8,16 +8,16 @@
 import pandas as pd
 import pytest
 
-from pelinker.config import (
+from pelinker.core.config import (
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
     LinkerFitConfig,
     ManifoldOovScreenerConfig,
     TransformConfig,
 )
-from pelinker.entity_head import fit_linear_svc_entity_head, fit_mlp_entity_head
+from pelinker.linker.entity_head import fit_linear_svc_entity_head, fit_mlp_entity_head
 from pelinker.model import Linker
-from pelinker.transform import is_parametric_umap
+from pelinker.clustering.transform import is_parametric_umap
 
 
 def test_mlp_entity_head_recovers_blob_labels() -> None:
diff --git a/test/test_eval_harness.py b/test/test_eval_harness.py
new file mode 100644
index 0000000..cbf9fbb
--- /dev/null
+++ b/test/test_eval_harness.py
@@ -0,0 +1,235 @@
+"""Gold-evaluation harness: document loading, both scoring regimes, lexical baseline."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from pelinker.eval.baselines import LexicalLemmaBaseline, LexicalSpanProposer
+from pelinker.eval.harness import (
+    GoldDoc,
+    evaluate_end_to_end,
+    evaluate_linking_only,
+    load_gold_docs,
+)
+
+TEXT_A = "TAMs secrete IL-10 in the tumor microenvironment."
+TEXT_B = "IL-6 is activated by TAMs."
+
+
+@pytest.fixture
+def gold_file(tmp_path: Path) -> Path:
+    p = tmp_path / "gold.json"
+    p.write_text(
+        json.dumps(
+            [
+                {
+                    "doc_id": 7,
+                    "text": TEXT_A,
+                    "ground_truth": [
+                        {
+                            "a": 5,
+                            "b": 12,
+                            "entity_id": "PEL.1",
+                            "direction": "forward",
+                        }
+                    ],
+                },
+                {
+                    "doc_id": 9,
+                    "text": TEXT_B,
+                    "ground_truth": [
+                        {
+                            "a": 5,
+                            "b": 20,
+                            "entity_id": "PEL.2",
+                            "direction": "inverse",
+                        }
+                    ],
+                },
+            ]
+        )
+    )
+    return p
+
+
+def test_load_gold_docs_groups_spans_by_document(gold_file: Path) -> None:
+    docs = load_gold_docs(gold_file)
+
+    assert [d.doc_id for d in docs] == [7, 9]
+    assert [d.text for d in docs] == [TEXT_A, TEXT_B]
+    # itext follows position in the file, matching what predictions are indexed by.
+    assert docs[0].spans[0].itext == 0
+    assert docs[1].spans[0].itext == 1
+    assert docs[1].spans[0].direction == "inverse"
+
+
+def test_end_to_end_scores_a_perfect_predictor(gold_file: Path) -> None:
+    docs = load_gold_docs(gold_file)
+
+    def predict(texts: list[str]) -> list[dict]:
+        assert texts == [TEXT_A, TEXT_B]
+        return [
+            {"itext": 0, "a": 5, "b": 12, "entity_id_predicted": "PEL.1"},
+            {"itext": 1, "a": 5, "b": 20, "entity_id_predicted": "PEL.2"},
+        ]
+
+    run = evaluate_end_to_end(predict, docs, system="oracle")
+
+    assert run.regime == "end_to_end"
+    assert run.n_docs == 2
+    assert run.wall_seconds >= 0.0
+    assert run.score["f1"] == 1.0
+    assert run.score["entity_accuracy"] == 1.0
+
+
+def test_end_to_end_applies_the_kb_out_id_bridge(gold_file: Path) -> None:
+    docs = load_gold_docs(gold_file)
+
+    def predict(texts: list[str]) -> list[dict]:
+        return [{"itext": 0, "a": 5, "b": 12, "entity_id_predicted": "kb::C0003"}]
+
+    run = evaluate_end_to_end(
+        predict,
+        docs,
+        system="linker",
+        predicted_id_to_kb_in={"kb::C0003": "PEL.1"},
+    )
+
+    assert run.score["n_id_comparable"] == 1
+    assert run.score["entity_accuracy"] == 1.0
+
+
+def test_linking_only_counts_abstentions_as_wrong_overall(gold_file: Path) -> None:
+    docs = load_gold_docs(gold_file)
+
+    def link(text: str, a: int, b: int) -> str | None:
+        return "PEL.1" if text.startswith("TAMs") else None
+
+    run = evaluate_linking_only(link, docs, system="half-abstaining")
+
+    assert run.regime == "linking_only"
+    assert run.score["n_spans"] == 2
+    assert run.score["n_predicted"] == 1
+    assert run.score["coverage"] == 0.5
+    # Abstention is free on the predicted-only view and costly on the overall one.
+    assert run.score["accuracy_on_predicted"] == 1.0
+    assert run.score["accuracy_overall"] == 0.5
+
+
+def test_linking_only_is_undefined_without_gold_ids() -> None:
+    docs = [GoldDoc(doc_id=0, text="x y", spans=())]
+
+    run = evaluate_linking_only(lambda t, a, b: "PEL.1", docs, system="none")
+
+    assert run.score["accuracy_overall"] is None
+
+
+def test_eval_run_serializes_flat(gold_file: Path) -> None:
+    docs = load_gold_docs(gold_file)
+    run = evaluate_linking_only(lambda t, a, b: None, docs, system="abstainer")
+
+    payload = run.to_jsonable()
+
+    json.dumps(payload)
+    assert payload["system"] == "abstainer"
+    assert payload["regime"] == "linking_only"
+    assert payload["coverage"] == 0.0
+
+
+# --------------------------------------------------------------- lexical baseline
+
+
+@pytest.fixture
+def kb_frame() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "entity_id": ["PEL.1", "PEL.2", "PEL.9"],
+            "label": ["secretes", "is activated by", "secretes"],
+            "description": ["secretion relation", "converse of activates", "dup"],
+        }
+    )
+
+
+def test_lexical_baseline_proposes_and_links_spans(kb_frame, nlp) -> None:
+    baseline = LexicalLemmaBaseline(kb_frame, nlp)
+
+    rows = baseline.predict([TEXT_A, TEXT_B])
+
+    by_doc = {r["itext"]: r for r in rows}
+    assert by_doc[0]["entity_id_predicted"] == "PEL.1"
+    assert TEXT_A[by_doc[0]["a"] : by_doc[0]["b"]] == "secrete"
+    assert by_doc[1]["entity_id_predicted"] == "PEL.2"
+    assert TEXT_B[by_doc[1]["a"] : by_doc[1]["b"]] == "is activated by"
+
+
+def test_duplicate_labels_resolve_to_the_smallest_id(kb_frame, nlp) -> None:
+    """PEL.1 and PEL.9 share the label 'secretes'; the choice must be deterministic."""
+    baseline = LexicalLemmaBaseline(kb_frame, nlp)
+
+    assert baseline.link(TEXT_A, 5, 12) == "PEL.1"
+
+
+def test_longest_match_wins_over_its_own_sub_span(kb_frame, nlp) -> None:
+    proposer = LexicalSpanProposer(kb_frame, nlp)
+
+    spans = proposer.propose(TEXT_B)
+
+    # "is activated by" (3 tokens) must not be split into a shorter competing match.
+    assert [TEXT_B[a:b] for a, b, _ in spans] == ["is activated by"]
+
+
+def test_unmatched_span_links_to_nothing(kb_frame, nlp) -> None:
+    baseline = LexicalLemmaBaseline(kb_frame, nlp)
+
+    assert baseline.link("the tumor microenvironment", 4, 9) is None
+
+
+def test_wider_gold_span_backs_off_to_the_matching_sub_window(nlp) -> None:
+    """Gold annotates the whole phrase; the KB often carries only the bare form."""
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["PEL.5"],
+            "label": ["activated"],
+            "description": ["activation relation"],
+        }
+    )
+    baseline = LexicalLemmaBaseline(kb, nlp)
+
+    # Exact-only matching would abstain here and make the baseline a strawman.
+    assert baseline.link(TEXT_B, 5, 20) == "PEL.5"
+
+
+def test_linking_only_scores_in_canonical_space(gold_file: Path) -> None:
+    """A converse-member answer is the right relation under the other name."""
+    docs = load_gold_docs(gold_file)
+    converse_of = {
+        span.entity_id: "RO.2" for doc in docs for span in doc.spans if span.entity_id
+    }
+
+    def link(text: str, a: int, b: int) -> str:
+        return "RO.2"
+
+    canonical = {"RO.2": next(iter(converse_of))}
+    run = evaluate_linking_only(link, docs, system="stub", canonicalize=canonical)
+
+    assert run.score["n_predicted"] == run.score["n_spans"]
+    assert run.score["n_correct"] >= 1
+
+
+def test_linking_only_drops_ids_the_bridge_cannot_map(gold_file: Path) -> None:
+    docs = load_gold_docs(gold_file)
+
+    run = evaluate_linking_only(
+        lambda text, a, b: "kb::CUNKNOWN",
+        docs,
+        system="stub",
+        predicted_id_to_kb_in={"kb::C1": "PEL.1"},
+    )
+
+    # Unmappable ids are not abstentions and not errors: they are incomparable.
+    assert run.score["n_predicted"] == 0
+    assert run.score["n_correct"] == 0
diff --git a/test/test_eval_kb.py b/test/test_eval_kb.py
new file mode 100644
index 0000000..700d509
--- /dev/null
+++ b/test/test_eval_kb.py
@@ -0,0 +1,89 @@
+"""The canonical KB view shared by annotation and scoring."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from pelinker.eval.kb import (
+    canonical_id_map,
+    canonical_kb,
+    pending_review_pairs,
+    require_canonical_kb,
+    symmetric_ids,
+)
+
+
+@pytest.fixture
+def kb() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "entity_id": ["PEL.1", "RO.2", "PEL.3"],
+            "label": ["regulates", "regulated by", "secretes"],
+            "is_canonical": [True, False, True],
+            "canonical_entity_id": ["PEL.1", "PEL.1", "PEL.3"],
+            "needs_review": [True, True, False],
+        }
+    )
+
+
+def test_only_canonical_members_are_offered(kb) -> None:
+    assert list(canonical_kb(kb)["entity_id"]) == ["PEL.1", "PEL.3"]
+
+
+def test_the_map_folds_a_converse_member_onto_its_canonical_partner(kb) -> None:
+    assert canonical_id_map(kb) == {
+        "PEL.1": "PEL.1",
+        "RO.2": "PEL.1",
+        "PEL.3": "PEL.3",
+    }
+
+
+def test_a_kb_predating_the_derivation_is_refused(kb) -> None:
+    legacy = kb.drop(columns=["is_canonical", "canonical_entity_id"])
+
+    with pytest.raises(ValueError, match="is_canonical"):
+        require_canonical_kb(legacy)
+
+
+def test_the_refusal_names_the_pairs_file_when_one_exists(tmp_path: Path, kb) -> None:
+    """The reported failure: an *.inverse.csv was passed, with its pairs file right there."""
+    inverse = tmp_path / "properties.synthesis.2.inverse.csv"
+    inverse.write_text("entity_id,label\n", encoding="utf-8")
+    pairs = tmp_path / "properties.synthesis.2.pairs.csv"
+    pairs.write_text("entity_id,label\n", encoding="utf-8")
+    legacy = kb.drop(columns=["is_canonical", "canonical_entity_id"])
+
+    with pytest.raises(ValueError, match=str(pairs)):
+        require_canonical_kb(legacy, kb_csv_path=inverse)
+
+
+def test_without_a_sibling_the_refusal_names_the_derivation_script(
+    tmp_path: Path, kb
+) -> None:
+    lonely = tmp_path / "properties.synthesis.2.inverse.csv"
+    lonely.write_text("entity_id,label\n", encoding="utf-8")
+    legacy = kb.drop(columns=["is_canonical", "canonical_entity_id"])
+
+    with pytest.raises(ValueError, match="derive_inverse_pairs.py"):
+        require_canonical_kb(legacy, kb_csv_path=lonely)
+
+
+def test_unreviewed_pairs_are_reported_for_a_warning(kb) -> None:
+    assert list(pending_review_pairs(kb)["entity_id"]) == ["PEL.1", "RO.2"]
+
+
+def test_a_kb_without_the_review_column_has_nothing_pending(kb) -> None:
+    assert len(pending_review_pairs(kb.drop(columns=["needs_review"]))) == 0
+
+
+def test_symmetric_ids_reads_the_flag(kb) -> None:
+    flagged = kb.assign(is_symmetric=[False, False, True])
+
+    assert symmetric_ids(flagged) == frozenset({"PEL.3"})
+
+
+def test_a_kb_without_the_symmetry_column_marks_nothing(kb) -> None:
+    assert symmetric_ids(kb) == frozenset()
diff --git a/test/test_eval_llm.py b/test/test_eval_llm.py
new file mode 100644
index 0000000..733cada
--- /dev/null
+++ b/test/test_eval_llm.py
@@ -0,0 +1,262 @@
+"""Provider seam and response caching for the gold-pipeline LLM calls."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+from pelinker.eval import llm
+
+
+def test_cache_key_is_stable_and_provider_sensitive() -> None:
+    args = dict(model="m", system="s", user="u", json_mode=True)
+
+    a = llm.cache_key(provider="gemini", **args)
+    b = llm.cache_key(provider="gemini", **args)
+    c = llm.cache_key(provider="anthropic", **args)
+
+    assert a == b
+    # Switching provider must not replay another provider's cached answer.
+    assert a != c
+
+
+def test_cache_key_separates_json_mode() -> None:
+    args = dict(provider="gemini", model="m", system="s", user="u")
+
+    assert llm.cache_key(json_mode=True, **args) != llm.cache_key(
+        json_mode=False, **args
+    )
+
+
+def test_unknown_provider_is_rejected(tmp_path: Path) -> None:
+    with pytest.raises(ValueError, match="provider must be one of"):
+        llm.complete(system="s", user="u", cache_dir=tmp_path, provider="oracle")
+
+
+def test_cache_hit_returns_without_calling_the_provider(tmp_path: Path) -> None:
+    key = llm.cache_key(
+        provider="gemini",
+        model=llm.DEFAULT_MODEL,
+        system="sys",
+        user="usr",
+        json_mode=False,
+    )
+    (tmp_path / f"{key}.json").write_text(
+        json.dumps({"text": "cached answer", "provider": "gemini"}), encoding="utf-8"
+    )
+
+    # No credentials and no network here: a miss would raise, so returning proves the hit.
+    out = llm.complete(system="sys", user="usr", cache_dir=tmp_path)
+
+    assert out == "cached answer"
+
+
+def test_cache_miss_writes_the_response(tmp_path: Path, monkeypatch) -> None:
+    captured: dict[str, object] = {}
+
+    def fake_gemini(
+        *, model, system, user, max_output_tokens, json_mode, reasoning_effort=None
+    ):
+        captured.update(
+            model=model, json_mode=json_mode, max_output_tokens=max_output_tokens
+        )
+        return llm.LLMResponse(
+            text="fresh", model=model, provider="gemini", usage={"total_token_count": 7}
+        )
+
+    monkeypatch.setattr(llm, "_complete_gemini", fake_gemini)
+
+    out = llm.complete(
+        system="sys",
+        user="usr",
+        cache_dir=tmp_path,
+        model="gemini-test",
+        json_mode=True,
+        max_output_tokens=123,
+    )
+
+    assert out == "fresh"
+    assert captured == {
+        "model": "gemini-test",
+        "json_mode": True,
+        "max_output_tokens": 123,
+    }
+    written = list(tmp_path.glob("*.json"))
+    assert len(written) == 1
+    payload = json.loads(written[0].read_text(encoding="utf-8"))
+    assert payload["text"] == "fresh"
+    assert payload["provider"] == "gemini"
+    assert payload["usage"]["total_token_count"] == 7
+
+
+def test_second_call_replays_the_cache(tmp_path: Path, monkeypatch) -> None:
+    calls: list[int] = []
+
+    def fake_gemini(
+        *, model, system, user, max_output_tokens, json_mode, reasoning_effort=None
+    ):
+        calls.append(1)
+        return llm.LLMResponse(text="once", model=model, provider="gemini")
+
+    monkeypatch.setattr(llm, "_complete_gemini", fake_gemini)
+
+    first = llm.complete(system="s", user="u", cache_dir=tmp_path)
+    second = llm.complete(system="s", user="u", cache_dir=tmp_path)
+
+    assert (first, second) == ("once", "once")
+    assert len(calls) == 1  # an annotation batch is re-runnable for free
+
+
+def test_openai_is_a_selectable_provider(tmp_path: Path, monkeypatch) -> None:
+    """The agreement slice needs a second model family, so the seam has three providers."""
+    seen: dict[str, object] = {}
+
+    def fake_openai(
+        *, model, system, user, max_output_tokens, json_mode, reasoning_effort=None
+    ):
+        seen.update(model=model, system=system)
+        return llm.LLMResponse(text="[]", model=model, provider="openai")
+
+    monkeypatch.setattr(llm, "_complete_openai", fake_openai)
+
+    out = llm.complete(
+        system="sys",
+        user="usr",
+        cache_dir=tmp_path,
+        provider="openai",
+        model="a-model",
+    )
+
+    assert out == "[]"
+    assert seen == {"model": "a-model", "system": "sys"}
+    assert "openai" in llm.PROVIDERS
+
+
+def test_each_provider_caches_separately(tmp_path: Path, monkeypatch) -> None:
+    """One provider's answer must never be replayed for another's identical prompt."""
+    monkeypatch.setattr(
+        llm,
+        "_complete_openai",
+        lambda **kw: llm.LLMResponse(
+            text="from openai", model=kw["model"], provider="openai"
+        ),
+    )
+    monkeypatch.setattr(
+        llm,
+        "_complete_gemini",
+        lambda **kw: llm.LLMResponse(
+            text="from gemini", model=kw["model"], provider="gemini"
+        ),
+    )
+
+    first = llm.complete(
+        system="s", user="u", cache_dir=tmp_path, provider="openai", model="m"
+    )
+    second = llm.complete(
+        system="s", user="u", cache_dir=tmp_path, provider="gemini", model="m"
+    )
+
+    assert (first, second) == ("from openai", "from gemini")
+
+
+# ------------------------------------------------------------ truncation and effort
+
+
+def test_cache_key_without_effort_matches_keys_written_before_it_existed() -> None:
+    """Existing caches must keep replaying: an unset effort leaves the key unchanged."""
+    key = llm.cache_key(
+        provider="gemini", model="m", system="s", user="u", json_mode=True
+    )
+
+    assert key == "59f6ef5704f4363ec4b98f4c01f87966"
+    assert key == llm.cache_key(
+        provider="gemini",
+        model="m",
+        system="s",
+        user="u",
+        json_mode=True,
+        reasoning_effort=None,
+    )
+
+
+def test_cache_key_separates_reasoning_effort() -> None:
+    def key(effort: str | None) -> str:
+        return llm.cache_key(
+            provider="openai",
+            model="m",
+            system="s",
+            user="u",
+            json_mode=True,
+            reasoning_effort=effort,
+        )
+
+    assert len({key("low"), key("high"), key(None)}) == 3
+
+
+def test_reasoning_effort_reaches_the_provider(tmp_path: Path, monkeypatch) -> None:
+    seen: dict[str, object] = {}
+
+    def fake_openai(**kw):
+        seen["reasoning_effort"] = kw["reasoning_effort"]
+        return llm.LLMResponse(text="[]", model=kw["model"], provider="openai")
+
+    monkeypatch.setattr(llm, "_complete_openai", fake_openai)
+
+    llm.complete(
+        system="s",
+        user="u",
+        cache_dir=tmp_path,
+        provider="openai",
+        model="m",
+        reasoning_effort="low",
+    )
+
+    assert seen == {"reasoning_effort": "low"}
+
+
+def test_reasoning_effort_is_refused_where_the_provider_has_none(
+    tmp_path: Path,
+) -> None:
+    with pytest.raises(ValueError, match="reasoning_effort"):
+        llm.complete(
+            system="s",
+            user="u",
+            cache_dir=tmp_path,
+            provider="gemini",
+            reasoning_effort="low",
+        )
+
+
+def test_a_truncated_answer_is_raised_and_never_cached(
+    tmp_path: Path, monkeypatch
+) -> None:
+    """Cached, a cut-off answer would replay as an empty document on every re-run."""
+
+    def truncated(**kw):
+        raise llm.LLMIncompleteError("stopped at max_output_tokens")
+
+    monkeypatch.setattr(llm, "_complete_openai", truncated)
+
+    with pytest.raises(llm.LLMIncompleteError):
+        llm.complete(
+            system="s", user="u", cache_dir=tmp_path, provider="openai", model="m"
+        )
+
+    assert list(tmp_path.glob("*.json")) == []
+
+
+def test_an_empty_answer_is_raised_and_never_cached(
+    tmp_path: Path, monkeypatch
+) -> None:
+    monkeypatch.setattr(
+        llm,
+        "_complete_gemini",
+        lambda **kw: llm.LLMResponse(text="  ", model=kw["model"], provider="gemini"),
+    )
+
+    with pytest.raises(llm.LLMIncompleteError, match="empty"):
+        llm.complete(system="s", user="u", cache_dir=tmp_path)
+
+    assert list(tmp_path.glob("*.json")) == []
diff --git a/test/test_eval_reference.py b/test/test_eval_reference.py
new file mode 100644
index 0000000..89e3f62
--- /dev/null
+++ b/test/test_eval_reference.py
@@ -0,0 +1,115 @@
+"""The reference inventory's tier 0+1 fold and its equivalences file."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from pelinker.eval.reference import (
+    fold_map,
+    folded_symmetric_ids,
+    load_equivalences,
+)
+
+_REPO = Path(__file__).resolve().parents[1]
+
+
+def _kb() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "entity_id": ["R.1", "R.1i", "S.1", "S.1c", "X.1"],
+            "label": ["regulates", "regulated by", "binds to", "bound by", "causes"],
+            "is_symmetric": [False, False, True, True, False],
+            "is_canonical": [True, False, True, True, True],
+            "canonical_entity_id": ["R.1", "R.1", "S.1", "S.1c", "X.1"],
+        }
+    )
+
+
+def _equivalences(tmp_path: Path, rows: list[tuple[str, str, str]]) -> Path:
+    path = tmp_path / "equivalences.csv"
+    pd.DataFrame(
+        {
+            "entity_id": [a for a, _, _ in rows],
+            "equivalent_to": [b for _, b, _ in rows],
+            "tier": [tier for _, _, tier in rows],
+            "note": ["" for _ in rows],
+        }
+    ).to_csv(path, index=False)
+    return path
+
+
+def test_tier0_folds_a_converse_member_onto_its_canonical_member() -> None:
+    fold = fold_map(_kb())
+
+    assert fold["R.1i"] == "R.1"
+    # Without tier 1, a minted converse stays its own class.
+    assert fold["S.1c"] == "S.1c"
+
+
+def test_tier1_folds_kb_implied_identities(tmp_path: Path) -> None:
+    kb = _kb()
+    eq = load_equivalences(
+        _equivalences(tmp_path, [("S.1c", "S.1", "kb_implied")]), kb=kb
+    )
+
+    fold = fold_map(kb, eq)
+
+    assert fold["S.1c"] == "S.1"
+    assert folded_symmetric_ids(kb, fold) == frozenset({"S.1"})
+
+
+def test_equivalence_chains_resolve_to_their_end(tmp_path: Path) -> None:
+    kb = _kb()
+    eq = load_equivalences(
+        _equivalences(
+            tmp_path, [("S.1c", "S.1", "kb_implied"), ("S.1", "X.1", "kb_implied")]
+        ),
+        kb=kb,
+    )
+
+    assert fold_map(kb, eq)["S.1c"] == "X.1"
+
+
+def test_an_equivalence_cycle_is_refused(tmp_path: Path) -> None:
+    kb = _kb()
+    eq = load_equivalences(
+        _equivalences(
+            tmp_path, [("S.1c", "S.1", "kb_implied"), ("S.1", "S.1c", "kb_implied")]
+        ),
+        kb=kb,
+    )
+
+    with pytest.raises(ValueError, match="cycle"):
+        fold_map(kb, eq)
+
+
+@pytest.mark.parametrize(
+    ("rows", "message"),
+    [
+        ([("S.1c", "S.1", "adjudicated")], "unknown tier"),
+        ([("S.1", "S.1", "kb_implied")], "onto themselves"),
+        (
+            [("S.1c", "S.1", "kb_implied"), ("S.1c", "X.1", "kb_implied")],
+            "more than one",
+        ),
+        ([("NOPE.1", "S.1", "kb_implied")], "not in the KB"),
+    ],
+)
+def test_malformed_equivalences_are_refused(
+    tmp_path: Path, rows: list[tuple[str, str, str]], message: str
+) -> None:
+    with pytest.raises(ValueError, match=message):
+        load_equivalences(_equivalences(tmp_path, rows), kb=_kb())
+
+
+def test_the_shipped_equivalences_load_against_the_pairs_kb() -> None:
+    kb = pd.read_csv(_REPO / "data/derived/properties.synthesis.2.pairs.csv")
+    eq = load_equivalences(_REPO / "data/curated/equivalences.csv", kb=kb)
+
+    fold = fold_map(kb, eq)
+
+    assert fold["PEL.000040"] == "PEL.000002"
+    assert fold["PEL.000041"] == "PEL.000003"
diff --git a/test/test_extract_properties_ro.py b/test/test_extract_properties_ro.py
new file mode 100644
index 0000000..c560e69
--- /dev/null
+++ b/test/test_extract_properties_ro.py
@@ -0,0 +1,69 @@
+"""RO extraction: which properties count as symmetric."""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pytest
+
+rdflib = pytest.importorskip("rdflib")
+
+_MODULE_PATH = (
+    Path(__file__).resolve().parents[1]
+    / "run"
+    / "preprocessing"
+    / "extract_properties_ro.py"
+)
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("extract_properties_ro", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+epr = _load_module()
+
+_TTL = """
+@prefix owl: <http://www.w3.org/2002/07/owl#> .
+@prefix obo: <http://purl.obolibrary.org/obo/> .
+
+obo:RO_1 a owl:ObjectProperty, owl:SymmetricProperty .
+obo:RO_2 a owl:ObjectProperty ;
+    owl:propertyChainAxiom ( [ owl:inverseOf obo:RO_3 ] obo:RO_3 ) .
+obo:RO_3 a owl:ObjectProperty .
+obo:RO_4 a owl:ObjectProperty ;
+    owl:propertyChainAxiom ( obo:RO_3 obo:RO_5 ) .
+obo:RO_5 a owl:ObjectProperty .
+"""
+
+
+def _graph() -> "rdflib.Graph":
+    g = rdflib.Graph()
+    g.parse(data=_TTL, format="turtle")
+    return g
+
+
+def _ids(iris) -> set[str]:
+    return {epr.iri_to_entity_id(i) for i in iris}
+
+
+def test_declared_symmetric_properties_are_found() -> None:
+    assert "RO.1" in _ids(epr.symmetric_property_iris(_graph()))
+
+
+def test_an_inverse_then_forward_chain_is_symmetric_by_construction() -> None:
+    """ "a connected to b iff some c connects a and c connects b" reads the same both ways."""
+    assert "RO.2" in _ids(epr.symmetric_property_iris(_graph()))
+
+
+def test_an_ordinary_chain_is_not_symmetric() -> None:
+    found = _ids(epr.symmetric_property_iris(_graph()))
+
+    assert "RO.4" not in found
+    assert "RO.3" not in found
diff --git a/test/test_fit_cluster_viz_scope.py b/test/test_fit_cluster_viz_scope.py
index 1927316..5c53578 100644
--- a/test/test_fit_cluster_viz_scope.py
+++ b/test/test_fit_cluster_viz_scope.py
@@ -7,8 +7,9 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.analysis import drop_entities_with_few_mentions, split_by_negative_label
-from pelinker.config import (
+from pelinker.data.frames import drop_entities_with_few_mentions
+from pelinker.screener.evaluation import split_by_negative_label
+from pelinker.core.config import (
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
     LinkerFitConfig,
@@ -17,18 +18,17 @@
     TransformConfig,
 )
 from pelinker.model import Linker
-from pelinker.onto import NEGATIVE_LABEL
+from pelinker.core.onto import NEGATIVE_LABEL
 from pelinker.plotting import (
     CLUSTER_VIZ_MEMBERSHIP_COLUMNS,
     build_fit_cluster_viz_plot_df,
     filter_assignments_for_cluster_viz,
 )
-from pelinker.reporting import (
-    ClusteringHyperparameters,
-    ModelSelectionReport,
+from pelinker.reports.io import (
     read_clustering_report_json,
     write_clustering_report_json,
 )
+from pelinker.reports.schema import ClusteringHyperparameters, ModelSelectionReport
 
 _SUBSAMPLE_ROWS = 80
 
diff --git a/test/test_gold_review_sheet.py b/test/test_gold_review_sheet.py
new file mode 100644
index 0000000..a288841
--- /dev/null
+++ b/test/test_gold_review_sheet.py
@@ -0,0 +1,450 @@
+"""Agreement over partial second-annotator coverage, and the review round trip."""
+
+from __future__ import annotations
+
+import importlib.util
+import json
+import sys
+from pathlib import Path
+
+import pandas as pd
+import pytest
+from click.testing import CliRunner
+
+_MODULE_PATH = (
+    Path(__file__).resolve().parents[1] / "run" / "eval" / "gold_review_sheet.py"
+)
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("gold_review_sheet", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+grs = _load_module()
+
+TEXT_A = "TAMs secrete IL-10 and regulate IL-6 levels in the tumor."
+TEXT_B = "The kinase activates the receptor in cortical neurons."
+
+
+def _hit(a: int, b: int, entity_id: str, direction: str = "forward") -> dict:
+    return {"a": a, "b": b, "entity_id": entity_id, "direction": direction}
+
+
+def _doc(doc_id: int, text: str, hits: list[dict]) -> dict:
+    return {"doc_id": doc_id, "text": text, "ground_truth": hits}
+
+
+def _write(path: Path, docs: list[dict]) -> str:
+    path.write_text(json.dumps(docs), encoding="utf-8")
+    return str(path)
+
+
+# ------------------------------------------------------------------- agreement
+
+
+def test_kappa_ignores_documents_the_second_annotator_never_saw() -> None:
+    """The double slice is small; scoring A's whole file against it would sink κ."""
+    shared_a = _doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])
+    shared_b = _doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])
+    unseen = _doc(2, TEXT_B, [_hit(11, 20, "PEL.2"), _hit(25, 33, "PEL.3")])
+
+    report = grs.kappa_report([shared_a, unseen], [shared_b])
+
+    assert report["n_docs_shared"] == 1
+    # Only the shared document contributes; the unannotated one is absent, not disagreed.
+    assert report["n_pairs"] == 1
+    assert report["n_only_a"] == 0
+
+
+def test_spans_only_the_second_annotator_found_count_against_agreement() -> None:
+    docs_a = [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])]
+    docs_b = [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1"), _hit(22, 30, "PEL.2")])]
+
+    report = grs.kappa_report(docs_a, docs_b)
+
+    assert report["n_only_b"] == 1
+    assert report["n_pairs"] == 2
+    # Perfect agreement on the shared span alone would be 1.0; the extra span is a
+    # disagreement, so it must not be.
+    assert report["kappa_entity_id"] < 1.0
+
+
+def test_a_missed_span_and_an_extra_span_are_penalized_alike() -> None:
+    both = _hit(5, 12, "PEL.1")
+    extra = _hit(22, 30, "PEL.2")
+    a_misses = grs.kappa_report(
+        [_doc(1, TEXT_A, [both])], [_doc(1, TEXT_A, [both, extra])]
+    )
+    b_misses = grs.kappa_report(
+        [_doc(1, TEXT_A, [both, extra])], [_doc(1, TEXT_A, [both])]
+    )
+
+    assert a_misses["kappa_entity_id"] == b_misses["kappa_entity_id"]
+    assert (a_misses["n_only_b"], b_misses["n_only_a"]) == (1, 1)
+
+
+def test_identical_files_agree_completely() -> None:
+    docs = [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1"), _hit(22, 30, "PEL.2")])]
+
+    report = grs.kappa_report(docs, [dict(d) for d in docs])
+
+    assert report["n_only_a"] == report["n_only_b"] == 0
+    assert report["kappa_entity_id"] == pytest.approx(1.0)
+
+
+# ------------------------------------------------------------------ round trip
+
+
+@pytest.fixture
+def kb_csv(tmp_path: Path) -> str:
+    path = tmp_path / "kb.csv"
+    pd.DataFrame(
+        {
+            "entity_id": ["PEL.1", "PEL.2"],
+            "label": ["regulates", "secretes"],
+            "is_canonical": [True, True],
+            "canonical_entity_id": ["PEL.1", "PEL.2"],
+        }
+    ).to_csv(path, index=False)
+    return str(path)
+
+
+def _export(tmp_path: Path, kb_csv: str, gold: str, other: str | None) -> pd.DataFrame:
+    out = tmp_path / "review.tsv"
+    args = ["export", "--gold", gold, "--kb-csv-path", kb_csv, "--output", str(out)]
+    if other is not None:
+        args += ["--other", other]
+    result = CliRunner().invoke(grs.main, args)
+    assert result.exit_code == 0, result.output
+    return pd.read_csv(out, sep="\t").fillna("")
+
+
+def test_export_marks_uncovered_documents_rather_than_showing_disagreement(
+    tmp_path: Path, kb_csv: str
+) -> None:
+    gold = _write(
+        tmp_path / "a.json",
+        [
+            _doc(1, TEXT_A, [_hit(5, 12, "PEL.1")]),
+            _doc(2, TEXT_B, [_hit(11, 20, "PEL.2")]),
+        ],
+    )
+    other = _write(tmp_path / "b.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+
+    sheet = _export(tmp_path, kb_csv, gold, other)
+
+    uncovered = sheet.loc[sheet["doc_id"] == 2].iloc[0]
+    assert uncovered["agrees"] == "not-covered"
+    covered = sheet.loc[sheet["doc_id"] == 1].iloc[0]
+    assert covered["agrees"] == "True"
+
+
+def test_export_surfaces_spans_only_the_second_annotator_proposed(
+    tmp_path: Path, kb_csv: str
+) -> None:
+    gold = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+    other = _write(
+        tmp_path / "b.json",
+        [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1"), _hit(22, 30, "PEL.2")])],
+    )
+
+    sheet = _export(tmp_path, kb_csv, gold, other)
+
+    proposed = sheet.loc[sheet["origin"] == "other"]
+    assert len(proposed) == 1
+    assert proposed.iloc[0]["entity_id"] == "PEL.2"
+    assert "【" in proposed.iloc[0]["context"]  # the adjudicator can see the mention
+
+
+def test_accepting_a_proposed_span_adds_it_to_verified_gold(
+    tmp_path: Path, kb_csv: str
+) -> None:
+    gold = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+    other = _write(
+        tmp_path / "b.json",
+        [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1"), _hit(22, 30, "PEL.2")])],
+    )
+    sheet_df = _export(tmp_path, kb_csv, gold, other)
+    sheet_df["verdict"] = "accept"
+    sheet_path = tmp_path / "filled.tsv"
+    sheet_df.to_csv(sheet_path, sep="\t", index=False)
+    out = tmp_path / "verified.json"
+
+    result = CliRunner().invoke(
+        grs.main,
+        [
+            "import",
+            "--gold",
+            gold,
+            "--sheet",
+            str(sheet_path),
+            "--other",
+            other,
+            "--verified-by",
+            "curator",
+            "--output",
+            str(out),
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    hits = json.loads(out.read_text(encoding="utf-8"))[0]["ground_truth"]
+    assert [h["entity_id"] for h in hits] == ["PEL.1", "PEL.2"]
+    assert {h["source"] for h in hits} == {"human"}
+    assert {h["annotator"] for h in hits} == {"curator"}
+
+
+def test_importing_a_proposed_span_without_its_source_file_is_refused(
+    tmp_path: Path, kb_csv: str
+) -> None:
+    """The hit lives in the other annotator's file; silently dropping it would bias recall."""
+    gold = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+    other = _write(
+        tmp_path / "b.json",
+        [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1"), _hit(22, 30, "PEL.2")])],
+    )
+    sheet_df = _export(tmp_path, kb_csv, gold, other)
+    sheet_df["verdict"] = "accept"
+    sheet_path = tmp_path / "filled.tsv"
+    sheet_df.to_csv(sheet_path, sep="\t", index=False)
+
+    result = CliRunner().invoke(
+        grs.main,
+        [
+            "import",
+            "--gold",
+            gold,
+            "--sheet",
+            str(sheet_path),
+            "--verified-by",
+            "curator",
+            "--output",
+            str(tmp_path / "verified.json"),
+        ],
+    )
+
+    assert result.exit_code != 0
+    assert "--other" in result.output
+
+
+def test_rejecting_a_proposed_span_leaves_gold_untouched(
+    tmp_path: Path, kb_csv: str
+) -> None:
+    gold = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+    other = _write(
+        tmp_path / "b.json",
+        [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1"), _hit(22, 30, "PEL.2")])],
+    )
+    sheet_df = _export(tmp_path, kb_csv, gold, other)
+    sheet_df["verdict"] = sheet_df["origin"].map({"gold": "accept", "other": "reject"})
+    sheet_path = tmp_path / "filled.tsv"
+    sheet_df.to_csv(sheet_path, sep="\t", index=False)
+    out = tmp_path / "verified.json"
+
+    result = CliRunner().invoke(
+        grs.main,
+        [
+            "import",
+            "--gold",
+            gold,
+            "--sheet",
+            str(sheet_path),
+            "--verified-by",
+            "curator",
+            "--output",
+            str(out),
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    hits = json.loads(out.read_text(encoding="utf-8"))[0]["ground_truth"]
+    assert [h["entity_id"] for h in hits] == ["PEL.1"]
+
+
+def test_import_writes_a_correction_summary_split_by_surface_type(
+    tmp_path: Path, kb_csv: str
+) -> None:
+    """The paper reports how much a curator changed, per anchored/paraphrase slice."""
+    anchored = {**_hit(5, 12, "PEL.1"), "surface_anchored": True}
+    paraphrase = {**_hit(22, 30, "PEL.2"), "surface_anchored": False}
+    gold = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [anchored, paraphrase])])
+    sheet_df = _export(tmp_path, kb_csv, gold, None)
+    assert set(sheet_df["surface_anchored"].astype(str)) == {"True", "False"}
+    sheet_df["verdict"] = ["accept", "reject"]
+    sheet_path = tmp_path / "filled.tsv"
+    sheet_df.to_csv(sheet_path, sep="\t", index=False)
+    out = tmp_path / "verified.json"
+
+    result = CliRunner().invoke(
+        grs.main,
+        [
+            "import",
+            "--gold",
+            gold,
+            "--sheet",
+            str(sheet_path),
+            "--verified-by",
+            "curator",
+            "--output",
+            str(out),
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    hits = json.loads(out.read_text(encoding="utf-8"))[0]["ground_truth"]
+    assert [h["verdict"] for h in hits] == ["accept"]
+    summary = json.loads(out.with_suffix(".verification.json").read_text())
+    assert summary["correction_rate"] == 0.5
+    assert summary["by_slice"] == {
+        "anchored": {"accept": 1, "fix": 0, "reject": 0},
+        "paraphrase": {"accept": 0, "fix": 0, "reject": 1},
+    }
+
+
+# ------------------------------------------------------- agreement in T̂ tier 0+1
+
+
+@pytest.fixture
+def pairs_kb_csv(tmp_path: Path) -> str:
+    """A converse pair, a symmetric relation, and its minted converse (tier-1 identity)."""
+    path = tmp_path / "pairs.csv"
+    pd.DataFrame(
+        {
+            "entity_id": ["PEL.1", "PEL.1i", "PEL.2", "PEL.2c"],
+            "label": ["regulates", "regulated by", "binds to", "bound by"],
+            "is_symmetric": [False, False, True, True],
+            "is_canonical": [True, False, True, True],
+            "canonical_entity_id": ["PEL.1", "PEL.1", "PEL.2", "PEL.2c"],
+        }
+    ).to_csv(path, index=False)
+    return str(path)
+
+
+@pytest.fixture
+def equivalences_csv(tmp_path: Path) -> str:
+    path = tmp_path / "equivalences.csv"
+    pd.DataFrame(
+        {
+            "entity_id": ["PEL.2c"],
+            "equivalent_to": ["PEL.2"],
+            "tier": ["kb_implied"],
+            "note": ["minted converse of a symmetric verb"],
+        }
+    ).to_csv(path, index=False)
+    return str(path)
+
+
+def _export_folded(
+    tmp_path: Path, kb_csv: str, equivalences: str | None, gold: str, other: str
+) -> pd.DataFrame:
+    out = tmp_path / f"review.{'folded' if equivalences else 'raw'}.tsv"
+    args = ["export", "--gold", gold, "--other", other, "--kb-csv-path", kb_csv]
+    if equivalences is not None:
+        args += ["--equivalences", equivalences]
+    result = CliRunner().invoke(grs.main, [*args, "--output", str(out)])
+    assert result.exit_code == 0, result.output
+    return pd.read_csv(out, sep="\t", dtype=str).fillna("")
+
+
+def test_export_shows_the_other_annotators_label(
+    tmp_path: Path, pairs_kb_csv: str
+) -> None:
+    gold = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+    other = _write(tmp_path / "b.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.2")])])
+
+    row = _export_folded(tmp_path, pairs_kb_csv, None, gold, other).iloc[0]
+
+    assert row["other_label"] == "binds to"
+    assert row["agrees"] == "False"
+    # Direction is not compared across two different relations.
+    assert row["agrees_direction"] == ""
+
+
+def test_a_converse_member_and_its_canonical_member_agree(
+    tmp_path: Path, pairs_kb_csv: str
+) -> None:
+    """Tier 0: "regulated by" and "regulates" are one relation."""
+    gold = _write(
+        tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1", "inverse")])]
+    )
+    other = _write(
+        tmp_path / "b.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1i", "forward")])]
+    )
+
+    row = _export_folded(tmp_path, pairs_kb_csv, None, gold, other).iloc[0]
+
+    assert row["agrees"] == "True"
+    assert row["agrees_direction"] == "False"
+
+
+def test_kb_implied_identities_agree_only_when_supplied(
+    tmp_path: Path, pairs_kb_csv: str, equivalences_csv: str
+) -> None:
+    """Tier 1: "bound by" ≡ "binds to" once the equivalences file says so."""
+    gold = _write(
+        tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.2", "symmetric")])]
+    )
+    other = _write(
+        tmp_path / "b.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.2c", "forward")])]
+    )
+
+    without = _export_folded(tmp_path, pairs_kb_csv, None, gold, other).iloc[0]
+    folded = _export_folded(tmp_path, pairs_kb_csv, equivalences_csv, gold, other)
+
+    assert without["agrees"] == "False"
+    assert folded.iloc[0]["agrees"] == "True"
+    # A symmetric relation has no orientation to disagree on.
+    assert folded.iloc[0]["agrees_direction"] == "True"
+
+
+def test_folded_kappa_counts_two_names_of_one_relation_as_agreement() -> None:
+    docs_a = [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1"), _hit(22, 30, "PEL.2")])]
+    docs_b = [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1i"), _hit(22, 30, "PEL.2")])]
+    fold = {"PEL.1": "PEL.1", "PEL.1i": "PEL.1", "PEL.2": "PEL.2"}
+
+    report = grs.kappa_report(docs_a, docs_b, fold=fold)
+
+    assert report["kappa_entity_id"] < 1.0
+    assert report["kappa_entity_id_folded"] == pytest.approx(1.0)
+
+
+def test_agreement_command_refuses_equivalences_without_a_kb(
+    tmp_path: Path, equivalences_csv: str
+) -> None:
+    docs = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+
+    result = CliRunner().invoke(
+        grs.main,
+        [
+            "agreement",
+            "--gold",
+            docs,
+            "--other",
+            docs,
+            "--equivalences",
+            equivalences_csv,
+        ],
+    )
+
+    assert result.exit_code != 0
+    assert "--kb-csv-path" in result.output
+
+
+def test_export_never_overwrites_a_review_sheet(tmp_path: Path, kb_csv: str) -> None:
+    """A filled sheet holds the curator's verdicts; a re-export must not replace it."""
+    gold = _write(tmp_path / "a.json", [_doc(1, TEXT_A, [_hit(5, 12, "PEL.1")])])
+    out = tmp_path / "review.tsv"
+    out.write_text("filled verdicts", encoding="utf-8")
+
+    result = CliRunner().invoke(
+        grs.main,
+        ["export", "--gold", gold, "--kb-csv-path", kb_csv, "--output", str(out)],
+    )
+
+    assert result.exit_code != 0
+    assert out.read_text(encoding="utf-8") == "filled verdicts"
diff --git a/test/test_grid_export.py b/test/test_grid_export.py
index 0f2f25b..5f1a597 100644
--- a/test/test_grid_export.py
+++ b/test/test_grid_export.py
@@ -9,9 +9,9 @@
 
 import json
 
-from pelinker.clustering_grid import SmoothedGridOptimumResult
-from pelinker.config import ClusteringOptimizationConfig
-from pelinker.grid_export import (
+from pelinker.clustering.grid import SmoothedGridOptimumResult
+from pelinker.core.config import ClusteringOptimizationConfig
+from pelinker.search.grid_export import (
     GRID_COL_CHOSEN_MIN_CLUSTER_SIZE,
     apply_chosen_min_cluster_size_to_grid,
     grid_chosen_hyperparameters_to_jsonable,
@@ -19,11 +19,8 @@
     select_grid_points_at_chosen_min_cluster_size,
     write_grid_chosen_hyperparameters,
 )
-from pelinker.reporting import (
-    ClusteringHyperparameters,
-    ModelSelectionReport,
-    entity_negative_label_mask_01,
-)
+from pelinker.data.frames import entity_negative_label_mask_01
+from pelinker.reports.schema import ClusteringHyperparameters, ModelSelectionReport
 
 
 def _minimal_report(
@@ -86,9 +83,12 @@ def test_write_grid_chosen_hyperparameters(tmp_path: pathlib.Path) -> None:
         y_objective=(0.5, 0.6, 0.55),
         y_cluster_term=(-0.1, 0.0, -0.05),
         y_smooth=(0.52, 0.58, 0.56),
-        dy_dx=(0.01, 0.0, -0.01),
-        d2y_dx2=(0.0, 0.0, 0.0),
-        selection="plateau_derivative",
+        selection="one_se_paired",
+        argmax_min_cluster_size=30,
+        y_se=(0.02, 0.0, 0.03),
+        y_eligible=(False, True, True),
+        one_se_k=1.0,
+        n_samples=5,
     )
     cfg = ClusteringOptimizationConfig(grid_cluster_count_reward=0.05)
     path = tmp_path / "grid_chosen_hyperparameters.json"
@@ -109,9 +109,7 @@ def test_grid_chosen_hyperparameters_to_jsonable_sorted() -> None:
         y_objective=(0.5,),
         y_cluster_term=(0.0,),
         y_smooth=(0.5,),
-        dy_dx=(0.0,),
-        d2y_dx2=(0.0,),
-        selection="smoothed_argmax",
+        selection="argmax",
     )
     solved_b = SmoothedGridOptimumResult(
         chosen_min_cluster_size=25,
@@ -122,9 +120,7 @@ def test_grid_chosen_hyperparameters_to_jsonable_sorted() -> None:
         y_objective=(0.6,),
         y_cluster_term=(0.0,),
         y_smooth=(0.6,),
-        dy_dx=(0.0,),
-        d2y_dx2=(0.0,),
-        selection="smoothed_argmax",
+        selection="argmax",
     )
     doc = grid_chosen_hyperparameters_to_jsonable(
         {("z", "1"): solved_a, ("a", "2"): solved_b},
diff --git a/test/test_grid_geomean_one_se.py b/test/test_grid_geomean_one_se.py
new file mode 100644
index 0000000..6b0b0a2
--- /dev/null
+++ b/test/test_grid_geomean_one_se.py
@@ -0,0 +1,206 @@
+"""Statistical properties of the clipped-geomean objective and the paired one-SE rule.
+
+These pin the two problems the previous selector had: an objective whose ranking depended
+on the arbitrary scales of DBCV and ARI, and an uncertainty discount that could promote a
+grid point with a genuinely worse mean.
+"""
+
+import logging
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from pelinker.clustering.grid import (
+    build_paired_grid_scores,
+    combine_clipped_geomean,
+    geomean_std,
+    solve_optimal_min_cluster_size_from_metrics_dfs as solve,
+)
+
+GRID = list(range(10, 60, 5))
+
+
+def _sample(
+    dbcv: list[float] | np.ndarray,
+    ari: list[float] | np.ndarray,
+    *,
+    grid: list[int] | None = None,
+    n_clusters: list[float] | None = None,
+) -> pd.DataFrame:
+    g = grid if grid is not None else GRID
+    return pd.DataFrame(
+        {
+            "min_cluster_size": g,
+            "icm": [0.1] * len(g),
+            "n_clusters": n_clusters if n_clusters is not None else [50.0] * len(g),
+            "dbcv": list(dbcv),
+            "ari": list(ari),
+        }
+    )
+
+
+def _curves(seed: int, *, shared_offset_sd: float = 0.0) -> pd.DataFrame:
+    """DBCV saturating with size, ARI peaking mid-grid, plus a whole-curve sample offset."""
+    r = np.random.default_rng(seed)
+    x = np.array(GRID, dtype=float)
+    off = r.normal(0.0, shared_offset_sd)
+    dbcv = 0.20 + 0.30 * (1 - np.exp(-(x - 10) / 12)) + off + r.normal(0, 0.01, x.size)
+    ari = (
+        0.30 + 0.10 * np.exp(-(((x - 30) / 14) ** 2)) + off + r.normal(0, 0.01, x.size)
+    )
+    return _sample(dbcv, ari)
+
+
+# --------------------------------------------------------------------------- combiner
+
+
+def test_geomean_is_conjunctive() -> None:
+    """A high DBCV must not buy off a near-zero ARI, as an arithmetic mean would."""
+    lopsided = combine_clipped_geomean(np.array([0.9]), np.array([0.01]))[0]
+    balanced = combine_clipped_geomean(np.array([0.4]), np.array([0.35]))[0]
+    assert lopsided < balanced
+    # The arithmetic mean gets this backwards, which is what the old objective used.
+    assert 0.5 * (0.9 + 0.01) > 0.5 * (0.4 + 0.35)
+
+
+def test_geomean_clips_negative_metrics_to_zero() -> None:
+    assert combine_clipped_geomean(np.array([-0.3]), np.array([0.5]))[0] == 0.0
+    assert np.isnan(combine_clipped_geomean(np.array([np.nan]), np.array([0.5]))[0])
+
+
+def test_geomean_std_uses_relative_errors() -> None:
+    d, a, sd, sa = 0.36, 0.64, 0.036, 0.128
+    got = geomean_std(np.array([d]), np.array([sd]), np.array([a]), np.array([sa]))[0]
+    expected = 0.5 * np.sqrt(d * a) * np.sqrt((sd / d) ** 2 + (sa / a) ** 2)
+    assert got == pytest.approx(expected)
+    # A pinned-at-zero score carries no uncertainty.
+    assert (
+        geomean_std(np.array([0.0]), np.array([0.1]), np.array([0.5]), np.array([0.1]))[
+            0
+        ]
+        == 0.0
+    )
+
+
+# ------------------------------------------------------------------- scale invariance
+
+
+def test_chosen_mcs_is_invariant_to_rescaling_either_metric() -> None:
+    """The whole point of the geometric mean: no normalization step is needed."""
+    dfs = [_curves(s) for s in range(6)]
+    base = solve(dfs).chosen_min_cluster_size
+    for fd, fa in [(10.0, 0.1), (0.25, 4.0), (100.0, 100.0)]:
+        scaled = [d.assign(dbcv=d.dbcv * fd, ari=d.ari * fa) for d in dfs]
+        assert solve(scaled).chosen_min_cluster_size == base, (fd, fa)
+
+
+def test_rescaling_reorders_the_arithmetic_mean_it_replaced() -> None:
+    """Guards the premise: an unweighted average really is scale-dependent."""
+    d = np.array([0.60, 0.10])
+    a = np.array([0.10, 0.50])
+    assert np.argmax(0.5 * (d + a)) == 0
+    assert np.argmax(0.5 * (d * 0.1 + a)) == 1  # rescale DBCV -> different winner
+    # The geometric mean's ranking survives the same rescaling.
+    assert np.argmax(combine_clipped_geomean(d, a)) == np.argmax(
+        combine_clipped_geomean(d * 0.1, a)
+    )
+
+
+# ------------------------------------------------------------------------ one-SE rule
+
+
+def test_pairing_cancels_the_shared_per_sample_offset() -> None:
+    """Paired SEs must ignore variance common to every grid point."""
+    quiet = [_curves(s, shared_offset_sd=0.0) for s in range(8)]
+    noisy = [_curves(s, shared_offset_sd=0.15) for s in range(8)]
+
+    se_quiet = np.max(solve(quiet).y_se)
+    se_noisy = np.max(solve(noisy).y_se)
+    # A 0.15 whole-curve offset would dominate an unpaired SE (~0.15/sqrt(8) = 0.053);
+    # paired, it barely registers.
+    assert se_noisy < 4 * se_quiet
+    assert se_noisy < 0.02
+    assert solve(noisy).chosen_min_cluster_size == solve(quiet).chosen_min_cluster_size
+
+
+def test_one_se_is_stable_under_leave_one_sample_out() -> None:
+    dfs = [_curves(s, shared_offset_sd=0.12) for s in range(8)]
+    chosen = {
+        solve([d for j, d in enumerate(dfs) if j != i]).chosen_min_cluster_size
+        for i in range(len(dfs))
+    }
+    assert len(chosen) == 1, f"choice moved across leave-one-out folds: {chosen}"
+
+
+def test_slide_is_disabled_below_min_samples() -> None:
+    """Measured on this repo's grid exports: below ~6 samples the slide adds variance.
+
+    Leave-one-out over 3-sample exports gave sd 2.68 with the slide vs 1.59 without, because
+    a standard error estimated from two folds is itself mostly noise.
+    """
+    few = [_curves(s, shared_offset_sd=0.10) for s in range(4)]
+    guarded = solve(few, one_se_k=1.0, one_se_min_samples=6)
+    argmax = solve(few, one_se_k=0.0)
+    assert guarded.chosen_min_cluster_size == argmax.chosen_min_cluster_size
+    assert guarded.selection == "argmax"
+    assert guarded.one_se_k == 0.0
+
+    many = [_curves(s, shared_offset_sd=0.10) for s in range(8)]
+    assert solve(many, one_se_k=1.0, one_se_min_samples=6).one_se_k == 1.0
+
+
+def test_single_sample_degenerates_to_argmax() -> None:
+    """The per-sample fit path (search/selection.py) solves on exactly one sample."""
+    out = solve([_curves(0)])
+    assert out.selection == "argmax"
+    assert out.n_samples == 1
+    assert max(out.y_se) == 0.0
+    assert out.chosen_min_cluster_size == out.argmax_min_cluster_size
+
+
+# --------------------------------------------------------------------- ragged / NaN
+
+
+def test_ragged_grids_reindex_onto_the_union() -> None:
+    """Samples drop grid points that produced no clusters; the grid must not shrink."""
+    short = _sample([0.5] * 6, [0.4] * 6, grid=GRID[:6])
+    full = _curves(1)
+    out = solve([short, full])
+    assert [int(v) for v in out.x] == GRID
+
+
+def test_nan_metrics_do_not_poison_the_pooled_curve() -> None:
+    a = _curves(2)
+    b = _curves(3)
+    b.loc[b.min_cluster_size < 25, "dbcv"] = np.nan
+    out = solve([a, b])
+    assert np.all(np.isfinite(out.y_smooth))
+    assert len(out.x) == len(GRID)
+
+
+# ------------------------------------------------------------------------ degenerate
+
+
+def test_all_zero_ari_falls_back_to_dbcv_with_a_warning(caplog) -> None:
+    dfs = [_curves(s).assign(ari=0.0) for s in range(4)]
+    with caplog.at_level(logging.WARNING, logger="pelinker.clustering.grid"):
+        out = solve(dfs)
+    assert out.selection == "degenerate_fallback"
+    assert "flat" in caplog.text.lower()
+
+
+def test_missing_ari_falls_back_to_dbcv_only() -> None:
+    dfs = [_curves(s).assign(ari=np.nan) for s in range(4)]
+    paired = build_paired_grid_scores(dfs)
+    assert paired.objective == "dbcv"
+    assert not paired.degenerate  # expected, not an alarm
+    assert np.all(np.isfinite(solve(dfs).y_smooth))
+
+
+def test_negative_dbcv_is_clipped_and_counted() -> None:
+    dfs = [_curves(s) for s in range(3)]
+    dfs[0].loc[dfs[0].min_cluster_size < 20, "dbcv"] = -0.4
+    paired = build_paired_grid_scores(dfs)
+    assert paired.n_clipped == 2
+    assert np.all(paired.scores[0, :2] == 0.0)
diff --git a/test/test_ground_truth_scoring.py b/test/test_ground_truth_scoring.py
index 3c4b475..64fa16e 100644
--- a/test/test_ground_truth_scoring.py
+++ b/test/test_ground_truth_scoring.py
@@ -7,12 +7,12 @@
 
 import pytest
 
-from pelinker.ground_truth import (
+from pelinker.kb.ground_truth import (
     GtSpan,
     load_ground_truth_spans,
     score_predictions_against_ground_truth,
 )
-from pelinker.linker_kb_lemma import aggregate_kb_lemma_validation
+from pelinker.linker.kb_lemma import aggregate_kb_lemma_validation
 
 
 def _pred(a: int, b: int, eid: str | None = None, itext: int = 0) -> dict:
@@ -55,6 +55,63 @@ def test_zero_length_span_is_rejected() -> None:
         GtSpan(itext=0, a=5, b=5)
 
 
+def test_loads_direction_and_provenance_fields(tmp_path: Path) -> None:
+    p = tmp_path / "gt.json"
+    p.write_text(
+        json.dumps(
+            {
+                "text": "IL-6 is activated by TAMs",
+                "ground_truth": [
+                    {
+                        "a": 5,
+                        "b": 20,
+                        "entity_id": "PEL.000032",
+                        "direction": "inverse",
+                        "subject_span": [21, 25],
+                        "object_span": [0, 4],
+                        "surface": "is activated by",
+                        "annotator": "llm-a",
+                        "source": "llm",
+                        "confidence": 0.9,
+                    },
+                    # A legacy hit in the same file stays loadable.
+                    {"a": 0, "b": 4, "entity_id": "PEL.000001"},
+                ],
+            }
+        )
+    )
+
+    spans = load_ground_truth_spans(p)
+
+    assert spans[0].direction == "inverse"
+    assert spans[0].subject_span == (21, 25)
+    assert spans[0].object_span == (0, 4)
+    assert spans[0].source == "llm"
+    assert spans[0].confidence == 0.9
+    assert spans[1].direction is None
+    assert spans[1].subject_span is None
+
+
+def test_unknown_direction_is_rejected() -> None:
+    with pytest.raises(ValueError, match="direction must be one of"):
+        GtSpan(itext=0, a=0, b=3, direction="backwards")
+
+
+def test_malformed_subject_span_is_rejected(tmp_path: Path) -> None:
+    p = tmp_path / "gt.json"
+    p.write_text(
+        json.dumps(
+            {
+                "text": "x",
+                "ground_truth": [{"a": 0, "b": 3, "subject_span": [1]}],
+            }
+        )
+    )
+
+    with pytest.raises(ValueError, match="subject_span must be"):
+        load_ground_truth_spans(p)
+
+
 # ----------------------------------------------------------------------- detection
 
 
@@ -234,3 +291,53 @@ def test_unenriched_rows_lower_the_resolvable_rate_rather_than_the_match_rate()
     assert m.n_predicted == 2
     assert m.n_resolvable == 1
     assert m.match_rate == 1.0
+
+
+def test_a_converse_id_counts_as_the_relation_it_names() -> None:
+    """Gold is canonical; "regulated by" is the same relation seen from the other end."""
+    preds = [_pred(0, 5, "RO.2")]
+    gold = [_gold(0, 5, "PEL.1")]
+
+    s = score_predictions_against_ground_truth(
+        preds, gold, canonicalize={"RO.2": "PEL.1", "PEL.1": "PEL.1"}
+    )
+
+    assert s.n_id_comparable == 1
+    assert s.entity_accuracy == 1.0
+
+
+def test_canonicalization_does_not_excuse_a_genuinely_wrong_id() -> None:
+    preds = [_pred(0, 5, "PEL.9")]
+    gold = [_gold(0, 5, "PEL.1")]
+
+    s = score_predictions_against_ground_truth(
+        preds, gold, canonicalize={"RO.2": "PEL.1", "PEL.1": "PEL.1", "PEL.9": "PEL.9"}
+    )
+
+    assert (s.n_id_comparable, s.n_id_correct) == (1, 0)
+
+
+def test_an_id_outside_the_canonical_map_is_left_alone() -> None:
+    """An id the KB never mentions is not this map's to rewrite — it is still comparable."""
+    preds = [_pred(0, 5, "PEL.1")]
+    gold = [_gold(0, 5, "PEL.1")]
+
+    s = score_predictions_against_ground_truth(
+        preds, gold, canonicalize={"RO.2": "PEL.1"}
+    )
+
+    assert s.entity_accuracy == 1.0
+
+
+def test_canonicalization_applies_after_the_kb_out_bridge() -> None:
+    preds = [_pred(0, 5, "kb::C0007")]
+    gold = [_gold(0, 5, "PEL.1")]
+
+    s = score_predictions_against_ground_truth(
+        preds,
+        gold,
+        predicted_id_to_kb_in={"kb::C0007": "RO.2"},
+        canonicalize={"RO.2": "PEL.1"},
+    )
+
+    assert s.entity_accuracy == 1.0
diff --git a/test/test_import_weight.py b/test/test_import_weight.py
new file mode 100644
index 0000000..8468464
--- /dev/null
+++ b/test/test_import_weight.py
@@ -0,0 +1,99 @@
+"""Import weight and the torch/TensorFlow load-order guarantee.
+
+Two invariants, both learned from a crash: importing anything under ``pelinker`` must not
+drag in the ML stack, and the one module that pulls TensorFlow must import torch first.
+Violating either produces a SIGSEGV rather than an exception, so they are checked in
+subprocesses.
+"""
+
+from __future__ import annotations
+
+import subprocess
+import sys
+import textwrap
+
+import pytest
+
+LIGHT_MODULES = ("torch", "triton", "tensorflow", "keras", "umap")
+
+
+def _run(code: str) -> subprocess.CompletedProcess[str]:
+    return subprocess.run(
+        [sys.executable, "-c", textwrap.dedent(code)],
+        capture_output=True,
+        text=True,
+        timeout=300,
+    )
+
+
+def test_package_root_pulls_in_nothing_heavy() -> None:
+    result = _run(
+        f"""
+        import sys
+        import pelinker
+        heavy = [m for m in {LIGHT_MODULES!r} if m in sys.modules]
+        assert not heavy, heavy
+        print("ok")
+        """
+    )
+
+    assert result.returncode == 0, result.stderr[-2000:]
+    assert "ok" in result.stdout
+
+
+def test_the_gold_pipeline_imports_stay_light() -> None:
+    """A script that reads a CSV and calls an HTTP API must not import torch."""
+    result = _run(
+        f"""
+        import sys
+        from pelinker.eval.llm import complete
+        from pelinker.eval.harness import load_gold_docs
+        from pelinker.kb.ground_truth import GT_DIRECTIONS
+        heavy = [m for m in {LIGHT_MODULES!r} if m in sys.modules]
+        assert not heavy, heavy
+        print("ok")
+        """
+    )
+
+    assert result.returncode == 0, result.stderr[-2000:]
+    assert "ok" in result.stdout
+
+
+@pytest.mark.heavy
+def test_transform_import_survives_a_later_triton_import() -> None:
+    """The documented segfault: TensorFlow first, then ``import triton.runtime``.
+
+    ``pelinker.clustering.transform`` imports ``umap``, which loads TensorFlow, so it must
+    preload torch/triton beforehand. Without that, this subprocess dies with SIGSEGV
+    (-11) instead of failing an assertion.
+    """
+    result = _run(
+        """
+        import pelinker.clustering.transform  # noqa: F401
+        import triton.runtime  # noqa: F401
+        print("ok")
+        """
+    )
+
+    if result.returncode != 0 and "No module named" in result.stderr:
+        pytest.skip("torch/triton or umap not installed in this environment")
+    assert result.returncode == 0, f"exit {result.returncode} (-11 is SIGSEGV)"
+    assert "ok" in result.stdout
+
+
+@pytest.mark.heavy
+def test_gold_pipeline_imports_after_tensorflow_is_already_loaded() -> None:
+    """The reported failure: a debugger had TensorFlow in the process first."""
+    result = _run(
+        """
+        import tensorflow  # noqa: F401
+        from pelinker.eval.llm import complete  # noqa: F401
+        from pelinker.kb.ground_truth import GT_DIRECTIONS  # noqa: F401
+        print("ok")
+        """
+    )
+
+    if result.returncode != 0 and "No module named" in result.stderr:
+        pytest.skip("tensorflow not installed in this environment")
+    assert result.returncode == 0, f"exit {result.returncode} (-11 is SIGSEGV)"
+    assert "ok" in result.stdout
diff --git a/test/test_kb_classes.py b/test/test_kb_classes.py
new file mode 100644
index 0000000..aa7dbbe
--- /dev/null
+++ b/test/test_kb_classes.py
@@ -0,0 +1,158 @@
+"""Class views of the pairs KB: raw label, canonical relation, relation + direction."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.kb.classes import (
+    ENTITY_CLASS_COLUMN,
+    RELATION_COLUMN,
+    RELATION_DIRECTION_COLUMN,
+    KbClasses,
+    add_view_columns,
+    check_class_view,
+    reference_label_column,
+    relation_direction,
+)
+
+
+@pytest.fixture
+def kb() -> pd.DataFrame:
+    """``regulates`` has a converse entry; ``inhibits`` has none; ``associates`` is symmetric."""
+    return pd.DataFrame(
+        {
+            "entity_id": ["RO.1", "RO.2", "PEL.3", "PEL.4", "RO.5", "RO.6"],
+            "label": [
+                "regulates",
+                "regulated by",
+                "inhibits",
+                "associates",
+                "has part",
+                "part of",
+            ],
+            "is_canonical": [True, False, True, True, True, False],
+            "canonical_entity_id": ["RO.1", "RO.1", "PEL.3", "PEL.4", "RO.5", "RO.5"],
+            "is_symmetric": [False, False, False, True, False, False],
+        }
+    )
+
+
+@pytest.fixture
+def classes(kb) -> KbClasses:
+    return KbClasses.from_kb(kb)
+
+
+def _mentions(rows: list[tuple[str, str | None]]) -> pd.DataFrame:
+    return pd.DataFrame(
+        {"entity": [label for label, _ in rows], "direction": [d for _, d in rows]}
+    )
+
+
+def test_passives_share_one_class_whether_or_not_the_kb_has_a_converse_entry(
+    classes,
+) -> None:
+    """The matcher stores a passive under the converse entry when one exists, else under
+    the active label with ``direction=inverse``. ``reldir`` must not tell those apart."""
+    frame = _mentions(
+        [
+            ("regulated by", "forward"),  # passive of regulates, via its converse entry
+            ("inhibits", "inverse"),  # passive of inhibits, which has no converse entry
+            ("regulates", "forward"),
+        ]
+    )
+
+    out = add_view_columns(frame, classes, "reldir")
+
+    assert out[ENTITY_CLASS_COLUMN].tolist() == [
+        "regulates|inverse",
+        "inhibits|inverse",
+        "regulates|forward",
+    ]
+    assert out[RELATION_COLUMN].tolist() == ["regulates", "inhibits", "regulates"]
+    assert out[RELATION_DIRECTION_COLUMN].tolist() == ["inverse", "inverse", "forward"]
+
+
+def test_raw_view_keeps_the_matched_label(classes) -> None:
+    frame = _mentions([("regulated by", "forward"), ("inhibits", "inverse")])
+
+    out = add_view_columns(frame, classes, "raw")
+
+    assert out[ENTITY_CLASS_COLUMN].tolist() == ["regulated by", "inhibits"]
+    # The entity column itself is never rewritten by any view.
+    assert out["entity"].tolist() == ["regulated by", "inhibits"]
+
+
+def test_rel_view_drops_direction(classes) -> None:
+    frame = _mentions([("regulated by", "forward"), ("regulates", "forward")])
+
+    out = add_view_columns(frame, classes, "rel")
+
+    assert out[ENTITY_CLASS_COLUMN].tolist() == ["regulates", "regulates"]
+
+
+def test_a_symmetric_relation_is_never_flipped(classes) -> None:
+    assert relation_direction("associates", "inverse", classes) == "symmetric"
+    out = add_view_columns(_mentions([("associates", "inverse")]), classes, "reldir")
+    assert out[ENTITY_CLASS_COLUMN].tolist() == ["associates"]
+
+
+def test_a_lexical_match_reads_its_own_label_forward(classes) -> None:
+    """A non-verb match carries no direction; it uses the label's own wording, so a
+    converse label ("part of") is the canonical relation read the other way."""
+    out = add_view_columns(
+        _mentions([("part of", None), ("has part", None)]), classes, "reldir"
+    )
+
+    assert out[ENTITY_CLASS_COLUMN].tolist() == ["has part|inverse", "has part|forward"]
+
+
+def test_the_negative_label_passes_through_and_unknown_labels_are_refused(
+    classes,
+) -> None:
+    frame = _mentions([(NEGATIVE_LABEL, None), ("regulates", "forward")])
+    out = add_view_columns(
+        frame, classes, "reldir", passthrough_labels=frozenset({NEGATIVE_LABEL})
+    )
+    assert out[ENTITY_CLASS_COLUMN].tolist() == [NEGATIVE_LABEL, "regulates|forward"]
+
+    with pytest.raises(ValueError, match="not in the class KB"):
+        add_view_columns(_mentions([("binds", "forward")]), classes, "reldir")
+
+
+def test_a_kb_without_the_pair_derivation_is_refused() -> None:
+    plain = pd.DataFrame({"entity_id": ["RO.1"], "label": ["regulates"]})
+    with pytest.raises(ValueError, match="predates the converse-pair derivation"):
+        KbClasses.from_kb(plain)
+
+
+def test_duplicate_labels_are_refused(kb) -> None:
+    dup = pd.concat([kb, kb.iloc[[0]].assign(entity_id="RO.9")], ignore_index=True)
+    with pytest.raises(ValueError, match="unique"):
+        KbClasses.from_kb(dup)
+
+
+def test_from_csv_reads_the_same_classes(kb, tmp_path: Path) -> None:
+    path = tmp_path / "kb.pairs.csv"
+    kb.to_csv(path, index=False)
+
+    assert KbClasses.from_csv(path) == KbClasses.from_kb(kb)
+
+
+def test_check_class_view() -> None:
+    assert check_class_view("raw", None) == "raw"
+    assert check_class_view("reldir", "kb.csv") == "reldir"
+    with pytest.raises(ValueError, match="needs --class-kb-path"):
+        check_class_view("reldir", None)
+    with pytest.raises(ValueError, match="must be one of"):
+        check_class_view("voice", "kb.csv")
+
+
+def test_reference_label_column_prefers_the_view(classes) -> None:
+    frame = _mentions([("regulates", "forward")])
+    assert reference_label_column(frame) == "entity"
+    viewed = add_view_columns(frame, classes, "reldir")
+    assert reference_label_column(viewed) == ENTITY_CLASS_COLUMN
diff --git a/test/test_kb_out.py b/test/test_kb_out.py
index 08ba2b0..b42bd77 100644
--- a/test/test_kb_out.py
+++ b/test/test_kb_out.py
@@ -3,9 +3,11 @@
 from __future__ import annotations
 
 import pandas as pd
+import pytest
 
-from pelinker.config import ClusterCompositionSnapshot, KBConfig
-from pelinker.kb_out import (
+from pelinker.core.config import ClusterCompositionSnapshot, KBConfig
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.kb.kb_out import (
     KbOutFitProvenance,
     KbOutNamingConfig,
     build_kb_out_catalog,
@@ -14,8 +16,9 @@
     find_split_mentions,
     format_cluster_display_name,
     format_kb_out_entity_id,
+    kb_out_to_kb_in_map,
 )
-from pelinker.reporting import read_kb_out_json, write_kb_out_json
+from pelinker.reports.io import read_kb_out_json, write_kb_out_json
 
 
 def test_format_cluster_display_name_top_components() -> None:
@@ -161,10 +164,68 @@ def test_find_split_mentions_homonymy() -> None:
     assert splits[0]["n_clusters"] == 2
 
 
+def test_kb_out_to_kb_in_map_resolves_dominant_labels_to_input_ids() -> None:
+    """The id bridge for gold scoring: minted cluster ids back onto input-KB ids."""
+    assignments = pd.DataFrame(
+        {
+            "entity": ["alpha", "alpha", "beta", NEGATIVE_LABEL, NEGATIVE_LABEL],
+            "cluster": [0, 0, 1, 2, 2],
+            "pmid": ["1", "1", "2", "3", "3"],
+            "mention": ["m", "m", "n", "x", "y"],
+            "cluster_score": [0.9, 0.85, 0.8, 0.7, 0.6],
+        }
+    )
+    composition = ClusterCompositionSnapshot(
+        global_property_mass={"alpha": 2, "beta": 1, NEGATIVE_LABEL: 2},
+        cluster_within_fraction={
+            0: {"alpha": 1.0},
+            1: {"beta": 1.0},
+            2: {NEGATIVE_LABEL: 1.0},
+        },
+        cluster_fraction_of_property_mass={
+            0: {"alpha": 1.0},
+            1: {"beta": 1.0},
+            2: {NEGATIVE_LABEL: 1.0},
+        },
+    )
+    catalog = build_kb_out_catalog(
+        composition,
+        assignments,
+        {"PEL.000002": "alpha", "PEL.000001": "alpha", "RO.0002206": "beta"},
+        kb_config=None,
+        fit_provenance=KbOutFitProvenance(min_cluster_size=2),
+    )
+
+    bridge = kb_out_to_kb_in_map(catalog)
+
+    by_cluster = {c["cluster_id"]: c["entity_id"] for c in catalog["clusters"]}
+    # Duplicate label "alpha": the lexicographically smallest id wins, deterministically.
+    assert bridge[by_cluster[0]] == "PEL.000001"
+    assert bridge[by_cluster[1]] == "RO.0002206"
+    # The negative-dominated cluster stays out: its predictions are non-comparable.
+    assert by_cluster[2] not in bridge
+
+
+def test_kb_out_to_kb_in_map_skips_labels_without_input_ids() -> None:
+    catalog = {
+        "provenance": {"kb_in": {"labels_map": {"PEL.1": "alpha"}}},
+        "clusters": [
+            {
+                "cluster_id": 0,
+                "entity_id": "kb::C0000",
+                "components": [{"entity": "unmapped label"}],
+            },
+            {"cluster_id": 1, "entity_id": "kb::C0001", "components": []},
+        ],
+    }
+
+    assert kb_out_to_kb_in_map(catalog) == {}
+
+
 def test_kb_out_json_round_trip(tmp_path) -> None:
     path = tmp_path / "kb_out.json"
     payload = {
-        "schema": "pelinker.kb_out.v1",
+        "schema": "pelinker.kb.kb_out.v1",
         "kb_out": {"entity_count": 1},
         "provenance": {"kb_in": {}, "fit": {"min_cluster_size": 2}},
         "naming": {"min_fraction": 0.05, "top_n": 3, "style": "weighted_dash"},
@@ -187,5 +248,38 @@ def test_kb_out_json_round_trip(tmp_path) -> None:
     }
     write_kb_out_json(path, payload)
     loaded = read_kb_out_json(path)
-    assert loaded["schema"] == "pelinker.kb_out.v1"
+    assert loaded["schema"] == "pelinker.kb.kb_out.v1"
     assert loaded["n_emergent_clusters"] == 1
+
+
+def test_cluster_direction_summary_breaks_ties_by_name_and_skips_noise() -> None:
+    from pelinker.kb.kb_out import cluster_direction_summary
+
+    assignments = pd.DataFrame(
+        {
+            "cluster": [0, 0, 1, 1, 1, -1],
+            "relation_direction": [
+                "inverse",
+                "forward",
+                "inverse",
+                "inverse",
+                "forward",
+                "forward",
+            ],
+        }
+    )
+
+    summary = cluster_direction_summary(assignments)
+
+    assert set(summary) == {0, 1}
+    assert summary[0]["dominant_direction"] == "forward"  # 1:1 tie → by name
+    assert summary[1]["dominant_direction"] == "inverse"
+    assert summary[1]["direction_mix"] == pytest.approx(
+        {"forward": 1 / 3, "inverse": 2 / 3}
+    )
+
+
+def test_cluster_direction_summary_is_empty_without_a_view() -> None:
+    from pelinker.kb.kb_out import cluster_direction_summary
+
+    assert cluster_direction_summary(pd.DataFrame({"cluster": [0, 1]})) == {}
diff --git a/test/test_kb_prompt.py b/test/test_kb_prompt.py
new file mode 100644
index 0000000..e1f2e72
--- /dev/null
+++ b/test/test_kb_prompt.py
@@ -0,0 +1,172 @@
+"""Rendering the KB catalog into a prompt: cleaning and the one-line-per-relation contract."""
+
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+
+from pelinker.eval.kb_prompt import clean_description, render_kb_catalog
+
+
+def _kb(**cols) -> pd.DataFrame:
+    base = {"entity_id": ["E1"], "label": ["regulates"], "description": [""]}
+    base.update(cols)
+    return pd.DataFrame(base)
+
+
+# ------------------------------------------------------------------- cleaning
+
+
+def test_newlines_are_collapsed() -> None:
+    """The defect that mattered: a multi-line description became several relations."""
+    raw = "X outer_layer_of Y iff:\n. X part_of Y\n. exists Z :surface\n"
+
+    cleaned = clean_description(raw)
+
+    assert "\n" not in cleaned
+    assert cleaned.startswith("X outer_layer_of Y iff: . X part_of Y")
+
+
+def test_formal_restatement_is_dropped() -> None:
+    raw = (
+        "x precedes y if and only if the time point at which x ends is before y. "
+        "Formally: x precedes y iff ω(x) <= α(y), where α maps a process to a start point."
+    )
+
+    cleaned = clean_description(raw)
+
+    assert cleaned.endswith("before y.")
+    for symbol in ("ω", "α", "Formally"):
+        assert symbol not in cleaned
+
+
+def test_pure_cross_references_are_dropped_entirely() -> None:
+    """They carry no definition and collide with the converse field."""
+    assert clean_description("inverse of develops from") == ""
+    assert clean_description("Inverse of characteristic_of") == ""
+
+
+def test_a_provenance_bracket_is_stripped_but_real_content_survives() -> None:
+    raw = "[copied from inverse property 'occurs in'] b occurs_in c =def b is a process"
+
+    cleaned = clean_description(raw)
+
+    assert cleaned == "b occurs_in c =def b is a process"
+
+
+def test_urls_and_padding_are_removed() -> None:
+    raw = "  Relation between a neuron and a structure. http://purl.obolibrary.org/obo/RO_1 \n "
+
+    assert clean_description(raw) == "Relation between a neuron and a structure."
+
+
+def test_missing_descriptions_are_empty_not_nan() -> None:
+    assert clean_description(None) == ""
+    assert clean_description(float("nan")) == ""
+    assert clean_description("") == ""
+
+
+def test_long_descriptions_truncate_on_a_word_boundary() -> None:
+    raw = "word " * 200
+
+    cleaned = clean_description(raw, max_chars=50)
+
+    assert len(cleaned) <= 51  # plus the ellipsis
+    assert cleaned.endswith("…")
+    assert "wor…" not in cleaned  # never mid-word
+
+
+# ------------------------------------------------------------------ rendering
+
+
+def test_one_line_per_relation_even_with_dirty_descriptions() -> None:
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["E1", "E2", "E3"],
+            "label": ["regulates", "bounding layer of", "secretes"],
+            "description": ["p regulates q", "a:\n. b\n. c\n", None],
+        }
+    )
+
+    lines = render_kb_catalog(kb).split("\n")
+
+    assert len(lines) == 3, lines
+
+
+def test_entries_are_numbered_and_labels_quoted() -> None:
+    kb = _kb(label=["has part"])
+
+    line = render_kb_catalog(kb)
+
+    assert line.startswith('1. "has part"')
+
+
+def test_converse_wording_is_shown_when_present() -> None:
+    kb = _kb(inverse_label=["regulated by"], description=["p regulates q"])
+
+    line = render_kb_catalog(kb)
+
+    assert 'converse wording: "regulated by"' in line
+    assert "def: p regulates q" in line
+
+
+def test_absent_converse_adds_no_dangling_field() -> None:
+    kb = _kb(inverse_label=[None])
+
+    line = render_kb_catalog(kb)
+
+    assert "converse wording" not in line
+
+
+def test_converse_can_be_suppressed() -> None:
+    kb = _kb(inverse_label=["regulated by"])
+
+    line = render_kb_catalog(kb, include_converse=False)
+
+    assert "converse wording" not in line
+
+
+def test_numbering_follows_row_order() -> None:
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["E1", "E2"],
+            "label": ["alpha", "beta"],
+            "description": ["", ""],
+        }
+    )
+
+    lines = render_kb_catalog(kb).split("\n")
+
+    assert lines[0].startswith('1. "alpha"')
+    assert lines[1].startswith('2. "beta"')
+
+
+@pytest.mark.parametrize("label", ["has  part", "has\npart"])
+def test_labels_with_stray_whitespace_are_normalized(label: str) -> None:
+    line = render_kb_catalog(_kb(label=[label]))
+
+    assert line == '1. "has part"'
+
+
+def test_the_shipped_kb_renders_one_line_per_relation() -> None:
+    """Guards the real artifact, not just synthetic rows."""
+    kb = pd.read_csv("data/derived/properties.synthesis.2.pairs.csv")
+    kb = kb.loc[kb["is_canonical"].astype(bool)]
+
+    lines = render_kb_catalog(kb).split("\n")
+
+    assert len(lines) == len(kb)
+
+
+def test_symmetric_relations_are_marked() -> None:
+    kb = _kb(
+        label=["interacts with", "regulates"],
+        entity_id=["E1", "E2"],
+        description=["", ""],
+        is_symmetric=[True, False],
+    )
+
+    first, second = render_kb_catalog(kb).split("\n")
+
+    assert first == '1. "interacts with" | symmetric'
+    assert "symmetric" not in second
diff --git a/test/test_kb_registry.py b/test/test_kb_registry.py
new file mode 100644
index 0000000..70f29cf
--- /dev/null
+++ b/test/test_kb_registry.py
@@ -0,0 +1,41 @@
+"""Version discovery over the derived KB directory."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from pelinker.kb.registry import fetch_latest_kb
+
+
+def _touch(directory: Path, *names: str) -> None:
+    for name in names:
+        (directory / name).write_text("entity_id,label\n", encoding="utf-8")
+
+
+def test_picks_the_highest_version(tmp_path: Path) -> None:
+    _touch(
+        tmp_path,
+        "properties.synthesis.0.csv",
+        "properties.synthesis.1.csv",
+        "properties.synthesis.10.csv",
+        "properties.synthesis.2.csv",
+    )
+
+    assert fetch_latest_kb(tmp_path) == ("properties.synthesis.10.csv", 10)
+
+
+def test_derived_variants_do_not_crash_discovery(tmp_path: Path) -> None:
+    """``*.synthesis.2.diverse.csv`` used to raise and break every KB regeneration."""
+    _touch(
+        tmp_path,
+        "properties.synthesis.2.csv",
+        "properties.synthesis.2.diverse.csv",
+        "properties.ro.csv",
+        "notes.txt",
+    )
+
+    assert fetch_latest_kb(tmp_path) == ("properties.synthesis.2.csv", 2)
+
+
+def test_empty_directory_reports_no_version(tmp_path: Path) -> None:
+    assert fetch_latest_kb(tmp_path) == (None, -1)
diff --git a/test/test_linker_fused_embeddings.py b/test/test_linker_fused_embeddings.py
index 6fdab8e..579918a 100644
--- a/test/test_linker_fused_embeddings.py
+++ b/test/test_linker_fused_embeddings.py
@@ -9,7 +9,7 @@
 
 from numpy.random import RandomState
 
-from pelinker.config import (
+from pelinker.core.config import (
     ClusteringOptimizationConfig,
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
@@ -17,13 +17,13 @@
     NegativeScreenerConfig,
 )
 from pelinker.model import Linker
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.reporting import (
-    LINKER_FIT_CLUSTERING_REPORT_BASENAME,
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.reports.io import (
     read_clustering_report_json,
     write_clustering_report_json,
 )
-from pelinker.transform import TransformConfig
+from pelinker.reports.paths import LINKER_FIT_CLUSTERING_REPORT_BASENAME
+from pelinker.clustering.transform import TransformConfig
 
 
 def _two_source_parquets(tmp_path, n_ent: int, pmids: tuple[str, ...]):
@@ -205,7 +205,7 @@ def test_fit_with_synthetic_negatives_screener_metrics_and_dump_load(tmp_path):
 
 
 def test_evaluate_selection_from_paths_multi_file_paths(tmp_path):
-    from pelinker.selection import evaluate_selection_from_paths
+    from pelinker.search.selection import evaluate_selection_from_paths
 
     p1 = tmp_path / "s0.parquet"
     p2 = tmp_path / "s1.parquet"
diff --git a/test/test_linker_kb_lemma.py b/test/test_linker_kb_lemma.py
index 8a77929..9ab14bc 100644
--- a/test/test_linker_kb_lemma.py
+++ b/test/test_linker_kb_lemma.py
@@ -7,12 +7,12 @@
 
 import torch
 
-from pelinker.linker_kb_lemma import (
+from pelinker.linker.kb_lemma import (
     enrich_entity_predictions_kb_validation,
     lookup_kb_training_entity_label,
 )
 from pelinker.model import Linker, LinkerPredictResult
-from pelinker.onto import MentionCandidate, WordGrouping
+from pelinker.core.onto import MentionCandidate, WordGrouping
 
 
 def test_lookup_kb_training_entity_label() -> None:
diff --git a/test/test_linker_screener_sampling.py b/test/test_linker_screener_sampling.py
index 3d8f00b..5c46475 100644
--- a/test/test_linker_screener_sampling.py
+++ b/test/test_linker_screener_sampling.py
@@ -5,9 +5,9 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.config import LinkerFitConfig
+from pelinker.core.config import LinkerFitConfig
 from pelinker.model import _screener_training_frame
-from pelinker.onto import NEGATIVE_LABEL
+from pelinker.core.onto import NEGATIVE_LABEL
 
 
 def _synthetic_frame(n_kb: int, n_neg: int) -> pd.DataFrame:
diff --git a/test/test_manifold_oov_screener.py b/test/test_manifold_oov_screener.py
index a6c3c5a..b40f034 100644
--- a/test/test_manifold_oov_screener.py
+++ b/test/test_manifold_oov_screener.py
@@ -4,8 +4,8 @@
 
 import numpy as np
 
-from pelinker.config import ManifoldOovScreenerConfig
-from pelinker.screener.projection_screener import (
+from pelinker.core.config import ManifoldOovScreenerConfig
+from pelinker.screener.projection import (
     ManifoldOovScoreModel,
     evaluate_projection_cv,
     fit_projection_score_model,
diff --git a/test/test_mentions.py b/test/test_mentions.py
new file mode 100644
index 0000000..26546e0
--- /dev/null
+++ b/test/test_mentions.py
@@ -0,0 +1,64 @@
+"""The shared mention detector: verb labels by voice, other labels lexically, one per site."""
+
+from __future__ import annotations
+
+import pytest
+
+from pelinker.text.mentions import LEXICAL_RULE, MentionDetector, keep_one_per_site
+from pelinker.text.tokenize import text_to_tokens
+
+LABELS = [
+    "activates",
+    "activated by",
+    "regulates",
+    "regulates transport of",
+    "part of",
+    "interacts with",
+    "controls",
+]
+
+
+@pytest.fixture(scope="module")
+def detector(nlp) -> MentionDetector:
+    return MentionDetector(LABELS, nlp, frozenset({"interacts with"}))
+
+
+def _detect(nlp, detector, text: str) -> list[tuple[str, str, str | None, str]]:
+    return [
+        (text[m.a : m.b], m.label, m.direction, m.rule)
+        for m in detector.detect(text_to_tokens(nlp, text))
+    ]
+
+
+def test_verb_and_lexical_labels_are_both_found(nlp, detector) -> None:
+    text = "NF-kB is activated by TNF, and Bcl-2 is part of the complex."
+
+    assert _detect(nlp, detector, text) == [
+        ("activated", "activated by", "forward", "passive_agent"),
+        ("part of", "part of", None, LEXICAL_RULE),
+    ]
+
+
+def test_a_noun_does_not_count_as_a_verb_mention(nlp, detector) -> None:
+    assert _detect(nlp, detector, "Patients were compared with healthy controls.") == []
+
+
+def test_a_longer_lexical_match_absorbs_the_verb_inside_it(nlp, detector) -> None:
+    text = "Insulin regulates transport of glucose."
+
+    assert [m[1] for m in _detect(nlp, detector, text)] == ["regulates transport of"]
+
+
+def test_verb_labels_are_split_from_lexical_ones(detector) -> None:
+    assert "part of" not in detector.verb_labels
+    assert {"activates", "activated by", "controls"} <= detector.verb_labels
+
+
+def test_keep_one_per_site_prefers_the_smaller_preference_on_a_tie() -> None:
+    items = [(0, 5, "b"), (0, 5, "a"), (10, 12, "c")]
+
+    kept = keep_one_per_site(
+        items, span=lambda x: (x[0], x[1]), preference=lambda x: (x[2],)
+    )
+
+    assert kept == [(0, 5, "a"), (10, 12, "c")]
diff --git a/test/test_model.py b/test/test_model.py
index 1862867..24b7db3 100644
--- a/test/test_model.py
+++ b/test/test_model.py
@@ -1,6 +1,6 @@
 import pytest
 
-from pelinker.onto import MAX_LENGTH
+from pelinker.core.onto import MAX_LENGTH
 from pelinker.model import Linker
 from importlib.resources import files
 
diff --git a/test/test_model_selection_summary.py b/test/test_model_selection_summary.py
index 3068a5a..3b93501 100644
--- a/test/test_model_selection_summary.py
+++ b/test/test_model_selection_summary.py
@@ -6,7 +6,7 @@
 
 import pandas as pd
 
-from pelinker.model_selection import build_model_selection_summary_payload
+from pelinker.search.model_selection import build_model_selection_summary_payload
 
 
 def test_build_model_selection_summary_payload_rankings() -> None:
@@ -80,7 +80,7 @@ def test_build_model_selection_summary_payload_rankings() -> None:
         chosen_by_combo=(("a", "l1", 10),),
         generated_at="2020-01-01T00:00:00Z",
     )
-    assert payload["schema"] == "pelinker.model_selection.summary.v1"
+    assert payload["schema"] == "pelinker.search.model_selection.summary.v1"
     assert payload["generated_at"] == "2020-01-01T00:00:00Z"
     top_scr = payload["rankings"]["top_by_screener_auc"]
     assert len(top_scr) == 3
diff --git a/test/test_negative_screener.py b/test/test_negative_screener.py
index 23caa53..a791f56 100644
--- a/test/test_negative_screener.py
+++ b/test/test_negative_screener.py
@@ -6,24 +6,24 @@
 import pandas as pd
 import pytest
 
-from pelinker.analysis import (
+from pelinker.screener.evaluation import (
     fit_ambient_screener_with_metrics,
     split_by_negative_label,
 )
-from pelinker.selection import evaluate_selection_sample
-from pelinker.config import ClusteringOptimizationConfig, NegativeScreenerConfig
-from pelinker.screener.ambient_screener import NegativeClassScreener
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.reporting import (
+from pelinker.search.selection import evaluate_selection_sample
+from pelinker.core.config import ClusteringOptimizationConfig, NegativeScreenerConfig
+from pelinker.screener.ambient import NegativeClassScreener
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.reports.schema import (
     AllScreenerCvResult,
     BinaryClassifierMetrics,
     ClusteringSearchSummaryRow,
     HyperparameterSearchStats,
     MeanWithUncertainty,
     MetricMeanStd,
-    clustering_search_summary_row_from_flat_dict,
 )
-from pelinker.transform import TransformConfig
+from pelinker.reports.summary import clustering_search_summary_row_from_flat_dict
+from pelinker.clustering.transform import TransformConfig
 
 
 def _tiny_frame(*, dim: int = 8, n_pos: int = 40, n_neg: int = 15) -> pd.DataFrame:
diff --git a/test/test_pca_quality_pairgrid_export.py b/test/test_pca_quality_pairgrid_export.py
index 91a9401..54f0d7b 100644
--- a/test/test_pca_quality_pairgrid_export.py
+++ b/test/test_pca_quality_pairgrid_export.py
@@ -11,7 +11,7 @@
     _balanced_subsample_by_class,
     plot_pca_quality_pairgrid,
 )
-from pelinker.model_selection.fine_metadata import (
+from pelinker.search.model_selection.fine_metadata import (
     fine_metadata_one_sample_per_combo,
     pca_pairgrid_output_path,
     safe_combo_plot_stem,
diff --git a/test/test_pca_residual_gating.py b/test/test_pca_residual_gating.py
index 1e44f8b..5d4ca2e 100644
--- a/test/test_pca_residual_gating.py
+++ b/test/test_pca_residual_gating.py
@@ -8,9 +8,9 @@
 
 from pelinker.cli import link_files
 from pelinker.model import Linker, LinkerPredictResult
-from pelinker.screener.ambient_screener import NegativeClassScreener
-from pelinker.onto import MentionCandidate, NEGATIVE_LABEL, WordGrouping
-from pelinker.transform import (
+from pelinker.screener.ambient import NegativeClassScreener
+from pelinker.core.onto import MentionCandidate, NEGATIVE_LABEL, WordGrouping
+from pelinker.clustering.transform import (
     EmbeddingTransformer,
     TransformConfig,
     compute_transform_artifacts,
diff --git a/test/test_plot_dbcv_ari_scatter.py b/test/test_plot_dbcv_ari_scatter.py
index 48ccb7c..9531ca8 100644
--- a/test/test_plot_dbcv_ari_scatter.py
+++ b/test/test_plot_dbcv_ari_scatter.py
@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from pelinker.grid_export import GRID_COL_CHOSEN_MIN_CLUSTER_SIZE
+from pelinker.search.grid_export import GRID_COL_CHOSEN_MIN_CLUSTER_SIZE
 from pelinker.plotting import _layer_spec_code, plot_dbcv_vs_ari_from_grid
 
 
@@ -54,7 +54,9 @@ def test_plot_dbcv_vs_ari_uses_chosen_mcs_not_other_grid_points(
     """Scatter must use (dbcv, ari) at chosen_min_cluster_size, not other grid rows."""
     df = _grid_df_for_combo(chosen_mcs=20)
     # If the plot used mcs=10 or arbitrary dedupe, means would differ from chosen-mcs values.
-    from pelinker.grid_export import select_grid_points_at_chosen_min_cluster_size
+    from pelinker.search.grid_export import (
+        select_grid_points_at_chosen_min_cluster_size,
+    )
 
     pts = select_grid_points_at_chosen_min_cluster_size(df)
     expected_x = float(pts["dbcv"].mean())
diff --git a/test/test_plot_grid_objective_panel.py b/test/test_plot_grid_objective_panel.py
index ac939f9..0b8ecf5 100644
--- a/test/test_plot_grid_objective_panel.py
+++ b/test/test_plot_grid_objective_panel.py
@@ -4,8 +4,10 @@
 
 import pandas as pd
 
-from pelinker.clustering_grid import SmoothedGridOptimumResult
-from pelinker.plotting import plot_metrics_with_error_bars
+from matplotlib import pyplot as plt
+
+from pelinker.clustering.grid import SmoothedGridOptimumResult
+from pelinker.plotting import plot_metrics, plot_metrics_with_error_bars
 
 
 def _synthetic_metrics_list() -> list[pd.DataFrame]:
@@ -38,12 +40,29 @@ def _synthetic_grid_solve() -> SmoothedGridOptimumResult:
         y_objective=(0.55, 0.58, 0.52),
         y_cluster_term=(-0.05, -0.02, -0.08),
         y_smooth=(0.54, 0.57, 0.53),
-        dy_dx=(0.01, 0.0, -0.02),
-        d2y_dx2=(0.0, 0.0, 0.0),
-        selection="plateau_derivative",
+        selection="one_se_paired",
+        argmax_min_cluster_size=30,
+        y_se=(0.02, 0.0, 0.03),
+        y_eligible=(False, True, True),
+        one_se_k=1.0,
+        n_samples=5,
     )
 
 
+def _objective_axis(monkeypatch) -> list[plt.Axes]:
+    """Capture the figures a plot call closes, so panels can be inspected after the fact."""
+    captured: list[plt.Figure] = []
+    real_close = plt.close
+
+    def _spy(fig):
+        if hasattr(fig, "axes"):
+            captured.append(fig)
+        return real_close(fig)
+
+    monkeypatch.setattr(plt, "close", _spy)
+    return captured
+
+
 def test_plot_metrics_with_error_bars_writes_four_panels_with_grid_solve(
     tmp_path: pathlib.Path,
 ) -> None:
@@ -58,14 +77,51 @@ def test_plot_metrics_with_error_bars_writes_four_panels_with_grid_solve(
     assert out.exists()
 
 
-def test_plot_metrics_with_error_bars_grid_solve_sets_vline(
-    tmp_path: pathlib.Path,
+def test_objective_panel_is_populated_on_the_live_run_path(
+    tmp_path: pathlib.Path, monkeypatch
 ) -> None:
-    out = tmp_path / "metrics.png"
-    solve = _synthetic_grid_solve()
+    """Regression: runners pass ``chosen_min_cluster_size`` and no ``grid_solve``.
+
+    That combination used to short-circuit the solve and leave the fourth axis empty with
+    an "(unavailable)" title in every real run — only the replot path rendered it.
+    """
+    figs = _objective_axis(monkeypatch)
     plot_metrics_with_error_bars(
         _synthetic_metrics_list(),
-        out,
-        grid_solve=solve,
+        tmp_path / "live.png",
+        chosen_min_cluster_size=20.0,
     )
+    assert figs, "no figure captured"
+    ax_obj = figs[-1].axes[3]
+    assert "unavailable" not in ax_obj.get_title().lower()
+    assert ax_obj.lines, "objective panel has no curves"
+
+
+def test_single_sample_plot_metrics_also_gets_an_objective_panel(
+    tmp_path: pathlib.Path, monkeypatch
+) -> None:
+    figs = _objective_axis(monkeypatch)
+    plot_metrics(_synthetic_metrics_list()[0], tmp_path / "single.png")
+    assert figs
+    fig = figs[-1]
+    assert len(fig.axes) == 4
+    ax_obj = fig.axes[3]
+    assert "unavailable" not in ax_obj.get_title().lower()
+    assert ax_obj.lines
+
+
+def test_plot_metrics_with_error_bars_grid_solve_sets_vline(
+    tmp_path: pathlib.Path, monkeypatch
+) -> None:
+    figs = _objective_axis(monkeypatch)
+    out = tmp_path / "metrics.png"
+    solve = _synthetic_grid_solve()
+    plot_metrics_with_error_bars(_synthetic_metrics_list(), out, grid_solve=solve)
     assert out.exists()
+    # The vline marks the solver's choice on every panel.
+    xs = {
+        round(line.get_xdata()[0], 6)
+        for line in figs[-1].axes[0].lines
+        if len(set(line.get_xdata())) == 1
+    }
+    assert float(solve.chosen_min_cluster_size) in xs
diff --git a/test/test_predicates.py b/test/test_predicates.py
new file mode 100644
index 0000000..5cab0e9
--- /dev/null
+++ b/test/test_predicates.py
@@ -0,0 +1,248 @@
+"""Voice-aware predicate detection: one label per verb mention, chosen by voice."""
+
+from __future__ import annotations
+
+import pytest
+
+from pelinker.core.onto import SimplifiedToken
+from pelinker.text.embed import LEXICAL_RULE, one_label_per_span
+from pelinker.text.predicates import (
+    PredicateMatcher,
+    is_passive_form,
+    predicate_spec,
+    verb_voice,
+)
+from pelinker.text.tokenize import text_to_tokens
+
+LABELS = [
+    "activates",
+    "activated by",
+    "regulates",
+    "regulated by",
+    "negatively regulates",
+    "negatively regulated by",
+    "controls",
+    "binds to",
+    "bound by",
+    "transmitted by",
+    "interacts with",
+    "connected to",
+    "connects",
+    "expresses",
+    "expressed in",
+]
+SYMMETRIC = {"interacts with", "connected to"}
+
+
+@pytest.fixture(scope="module")
+def matcher(nlp) -> PredicateMatcher:
+    specs = [
+        predicate_spec(label, nlp, symmetric=label in SYMMETRIC) for label in LABELS
+    ]
+    assert all(s is not None for s in specs), [
+        label for label, s in zip(LABELS, specs) if s is None
+    ]
+    return PredicateMatcher([s for s in specs if s is not None])
+
+
+def _matches(nlp, matcher, text: str) -> list[tuple[str, str, str, str]]:
+    tokens = text_to_tokens(nlp, text)
+    by_i = {t.i: t for t in tokens}
+    return [
+        (
+            text[by_i[m.start].ix : by_i[m.head].ix_end],
+            m.label,
+            m.direction,
+            m.rule,
+        )
+        for m in matcher.match(tokens)
+    ]
+
+
+@pytest.mark.parametrize(
+    "text, expected",
+    [
+        ("TNF activates NF-kB.", [("activates", "activates", "forward", "active")]),
+        (
+            "NF-kB is activated by TNF.",
+            [("activated", "activated by", "forward", "passive_agent")],
+        ),
+        # The agent is not adjacent: a lemma window would have labelled this `activates`.
+        (
+            "NF-kB was activated in vivo by TNF.",
+            [("activated", "activated by", "forward", "passive_agent")],
+        ),
+        (
+            "NF-kB was activated.",
+            [("activated", "activated by", "forward", "passive_agentless")],
+        ),
+        (
+            "IL-6 is negatively regulated by TAMs.",
+            [
+                (
+                    "negatively regulated",
+                    "negatively regulated by",
+                    "forward",
+                    "passive_agent",
+                )
+            ],
+        ),
+        (
+            "MDM2 is bound by p53.",
+            [("bound", "bound by", "forward", "passive_agent")],
+        ),
+        # No active entry for "transmit": the converse label, read the other way.
+        (
+            "Mosquitoes transmit the virus.",
+            [("transmit", "transmitted by", "inverse", "active")],
+        ),
+        (
+            "p53 interacts with MDM2.",
+            [("interacts", "interacts with", "symmetric", "active")],
+        ),
+        (
+            "A is connected to B.",
+            [("connected", "connected to", "symmetric", "passive_agentless")],
+        ),
+        (
+            "The protein activated by TNF was degraded.",
+            [("activated", "activated by", "forward", "passive_agent")],
+        ),
+    ],
+)
+def test_each_verb_mention_gets_one_label_by_voice(
+    nlp, matcher, text: str, expected
+) -> None:
+    assert _matches(nlp, matcher, text) == expected
+
+
+@pytest.mark.parametrize(
+    "text",
+    [
+        "Patients were compared with healthy controls.",  # noun, not the verb
+        "The activated T cells expanded.",  # prenominal participle: dropped
+    ],
+)
+def test_non_verbal_and_adjectival_uses_are_not_labelled(nlp, matcher, text) -> None:
+    assert _matches(nlp, matcher, text) == []
+
+
+@pytest.mark.parametrize(
+    "label",
+    ["is tautomer of", "coincident with", "has part", "part of", "is marker for"],
+)
+def test_labels_that_are_not_verb_predicates_have_no_spec(nlp, label: str) -> None:
+    assert predicate_spec(label, nlp) is None
+
+
+def test_a_passive_label_is_recognised_as_one(nlp) -> None:
+    spec = predicate_spec("negatively regulated by", nlp)
+
+    assert spec is not None
+    assert (spec.head_lemma, spec.modifiers, spec.prep, spec.passive) == (
+        "regulate",
+        ("negatively",),
+        "by",
+        True,
+    )
+
+
+def test_is_passive_form_needs_a_participle_and_a_preposition() -> None:
+    assert is_passive_form("bound by")
+    assert is_passive_form("expressed in")
+    assert not is_passive_form("binds to")
+    assert not is_passive_form("regulated")
+
+
+def _tok(
+    i: int, text: str, *, head: int, dep: str, tag: str = "VBN"
+) -> SimplifiedToken:
+    return SimplifiedToken(
+        ix=i * 10,
+        ix_end=i * 10 + len(text),
+        text=text,
+        lemma=text,
+        tag=tag,
+        pos="VERB",
+        i=i,
+        head_i=head,
+        dep=dep,
+    )
+
+
+def test_a_bare_past_participle_has_no_voice() -> None:
+    verb = _tok(0, "activated", head=0, dep="ROOT")
+
+    assert verb_voice(verb, []) is None
+
+
+def test_a_perfect_participle_is_active() -> None:
+    verb = _tok(1, "activated", head=1, dep="ROOT")
+    aux = _tok(0, "have", head=1, dep="aux", tag="VBZ")
+
+    assert verb_voice(verb, [aux]) == ("active", "active")
+
+
+def test_without_a_parse_nothing_is_matched() -> None:
+    token = SimplifiedToken(
+        ix=0, ix_end=9, text="activates", lemma="activate", tag="VBZ"
+    )
+
+    assert PredicateMatcher([]).match([token]) == []
+
+
+def _row(a: int, b: int, entity: str, rule: str) -> dict:
+    return {"a_abs": a, "b_abs": b, "entity": entity, "surface_rule": rule}
+
+
+def test_a_span_nested_in_a_longer_match_is_dropped() -> None:
+    rows = [
+        _row(0, 9, "regulates", "active"),
+        _row(0, 22, "regulates transport of", LEXICAL_RULE),
+    ]
+
+    assert [r["entity"] for r in one_label_per_span(rows)] == ["regulates transport of"]
+
+
+def test_on_an_identical_span_the_parse_label_wins() -> None:
+    rows = [
+        _row(0, 9, "activates", LEXICAL_RULE),
+        _row(0, 9, "activated by", "passive_agent"),
+    ]
+
+    assert [r["entity"] for r in one_label_per_span(rows)] == ["activated by"]
+
+
+def test_disjoint_mentions_are_all_kept() -> None:
+    rows = [_row(0, 5, "causes", "active"), _row(10, 16, "caused by", "passive_agent")]
+
+    assert len(one_label_per_span(rows)) == 2
+
+
+@pytest.mark.parametrize(
+    "text",
+    [
+        "Five patients developed complications.",  # not `develops from`
+        "Most deaths occur during labor.",  # not `occurs in`
+    ],
+)
+def test_a_label_preposition_must_attach_to_the_verb(nlp, text: str) -> None:
+    labels = ["develops from", "occurs in"]
+    specs = [predicate_spec(label, nlp) for label in labels]
+    matcher = PredicateMatcher([s for s in specs if s is not None])
+
+    assert matcher.match(text_to_tokens(nlp, text)) == []
+
+
+def test_the_preposition_is_matched_when_present(nlp) -> None:
+    spec = predicate_spec("develops from", nlp)
+    assert spec is not None
+    matcher = PredicateMatcher([spec])
+
+    found = matcher.match(text_to_tokens(nlp, "The tumour develops from stem cells."))
+
+    assert [m.label for m in found] == ["develops from"]
+
+
+def test_a_prenominal_modifier_without_an_object_is_not_a_mention(nlp, matcher) -> None:
+    assert _matches(nlp, matcher, "We found enrichment of binding sites.") == []
diff --git a/test/test_preprocess.py b/test/test_preprocess.py
index 9c7caeb..9f1f389 100644
--- a/test/test_preprocess.py
+++ b/test/test_preprocess.py
@@ -1,4 +1,4 @@
-from pelinker.preprocess import pre_process_properties
+from pelinker.kb.preprocess import pre_process_properties
 
 
 def test_prep(df_properties):
diff --git a/test/test_reporting_clustering.py b/test/test_reporting_clustering.py
index 87cbda2..d85f60c 100644
--- a/test/test_reporting_clustering.py
+++ b/test/test_reporting_clustering.py
@@ -8,22 +8,24 @@
 import pandas as pd
 import pytest
 
-from pelinker.onto import NEGATIVE_LABEL
+from pelinker.core.onto import NEGATIVE_LABEL
 from pelinker.plotting import diagnostics_to_pairgrid_dataframe
-from pelinker.reporting import (
-    ClusteringHyperparameters,
-    LinkerFitDiagnostics,
-    ModelSelectionReport,
-    ClusteringSearchSummaryRow,
-    entity_negative_label_mask_01,
+from pelinker.data.frames import entity_negative_label_mask_01
+from pelinker.reports.io import (
     read_cluster_composition_json,
     read_clustering_report_json,
-    subsample_diagnostics_stratified,
-    summarize_clustering_reports_for_search,
     write_cluster_composition_json,
     write_clustering_report_json,
 )
-from pelinker.cluster_composition_viz import build_cluster_composition_df
+from pelinker.reports.schema import (
+    ClusteringHyperparameters,
+    ClusteringSearchSummaryRow,
+    LinkerFitDiagnostics,
+    ModelSelectionReport,
+    subsample_diagnostics_stratified,
+)
+from pelinker.reports.summary import summarize_clustering_reports_for_search
+from pelinker.clustering.composition import build_cluster_composition_df
 
 
 def _minimal_report(
diff --git a/test/test_run_baselines_wiring.py b/test/test_run_baselines_wiring.py
new file mode 100644
index 0000000..1273d10
--- /dev/null
+++ b/test/test_run_baselines_wiring.py
@@ -0,0 +1,71 @@
+"""Wiring of the fitted-linker system in the baseline runner.
+
+`Linker.predict` takes `threshold`; the runner called it with `thr_score`, so the
+`linker` system raised `TypeError` on its first call and had never executed. Only
+`--systems lexical,encoder` had ever been run.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+_MODULE_PATH = Path(__file__).resolve().parents[1] / "run" / "eval" / "run_baselines.py"
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("run_baselines", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+rb = _load_module()
+
+
+class RecordingLinker:
+    """Accepts only the kwargs `Linker.predict` really has."""
+
+    def __init__(self) -> None:
+        self.calls: list[dict] = []
+
+    def predict(self, texts, threshold=0.0, **kwargs):
+        self.calls.append({"n_texts": len(texts), "threshold": threshold, **kwargs})
+
+        class _Result:
+            entities = [
+                {"itext": 0, "a": 0, "b": 4, "entity_id_predicted": "kb::C0001"}
+            ]
+
+        return _Result()
+
+
+def test_predict_receives_threshold_not_thr_score() -> None:
+    linker = RecordingLinker()
+
+    rows = rb._linker_predict_fn(linker, 0.25)(["some text", "other text"])
+
+    assert linker.calls[0]["threshold"] == 0.25
+    assert "thr_score" not in linker.calls[0]
+    assert rows and rows[0]["entity_id_predicted"] == "kb::C0001"
+
+
+def test_one_predict_call_covers_the_whole_batch() -> None:
+    linker = RecordingLinker()
+
+    rb._linker_predict_fn(linker, 0.0)(["a", "b", "c"])
+
+    assert len(linker.calls) == 1
+    assert linker.calls[0]["n_texts"] == 3
+
+
+def test_span_link_fn_also_uses_threshold() -> None:
+    linker = RecordingLinker()
+
+    out = rb._linker_link_fn(linker, 0.5)("some text", 0, 4)
+
+    assert linker.calls[0]["threshold"] == 0.5
+    assert out == "kb::C0001"
diff --git a/test/test_sample_gold.py b/test/test_sample_gold.py
new file mode 100644
index 0000000..849b99b
--- /dev/null
+++ b/test/test_sample_gold.py
@@ -0,0 +1,190 @@
+"""Identifier extraction and provenance for the gold sample."""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+_MODULE_PATH = Path(__file__).resolve().parents[1] / "run" / "eval" / "sample_gold.py"
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("sample_gold", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+sg = _load_module()
+
+FULL = (
+    "Row(doi='https://doi.org/10.1016/x', mag=2017017066, "
+    "openalex='https://openalex.org/W2017017066', pmcid=None, "
+    "pmid='https://pubmed.ncbi.nlm.nih.gov/12714192')"
+)
+NO_PMID = (
+    "Row(doi=None, mag=3586560, openalex='https://openalex.org/W3586560', "
+    "pmcid=None, pmid=None)"
+)
+
+
+def test_every_identifier_is_extracted() -> None:
+    ids = sg.extract_identifiers(FULL)
+
+    assert ids["doi"] == "https://doi.org/10.1016/x"
+    assert ids["mag"] == "2017017066"
+    assert ids["openalex"] == "https://openalex.org/W2017017066"
+    assert ids["pmcid"] is None
+    # A pmid URL is reduced to the numeric id used everywhere else.
+    assert ids["pmid"] == "12714192"
+
+
+def test_none_fields_become_none_not_the_string() -> None:
+    ids = sg.extract_identifiers(NO_PMID)
+
+    assert ids["doi"] is None and ids["pmid"] is None and ids["pmcid"] is None
+    assert ids["mag"] == "3586560"
+
+
+def test_doc_uid_prefers_pmid() -> None:
+    """The gold set is pmid-only, and a pmid is what a biomedical reader resolves."""
+    assert sg.document_uid(sg.extract_identifiers(FULL)) == "12714192"
+
+    # Without a pmid the remaining identifiers still order deterministically.
+    no_pmid = sg.extract_identifiers(NO_PMID)
+    assert sg.document_uid(no_pmid).endswith("W3586560")
+
+
+def test_doc_uid_is_none_when_nothing_identifies_the_row() -> None:
+    ids = sg.extract_identifiers(
+        "Row(doi=None, mag=None, openalex=None, pmcid=None, pmid=None)"
+    )
+
+    assert sg.document_uid(ids) is None
+
+
+def test_unparsable_ids_field_yields_no_identifier() -> None:
+    assert sg.document_uid(sg.extract_identifiers("")) is None
+    assert sg.document_uid(sg.extract_identifiers(float("nan"))) is None
+
+
+def test_id_field_precedence_is_explicit() -> None:
+    assert sg.ID_FIELDS == ("pmid", "doi", "openalex", "mag", "pmcid")
+
+
+@pytest.mark.parametrize(
+    "raw,expected",
+    [
+        ("Row(pmid='12714192')", "12714192"),
+        ("Row(pmid='https://pubmed.ncbi.nlm.nih.gov/999')", "999"),
+    ],
+)
+def test_pmid_accepts_bare_or_url_form(raw: str, expected: str) -> None:
+    assert sg.extract_identifiers(raw)["pmid"] == expected
+
+
+def test_the_drawn_sample_is_pmid_only_and_fully_identified() -> None:
+    """Guards the real artifact: every gold document must be citable by pmid."""
+    import pandas as pd
+
+    manifest = Path(
+        "~/data/pelinker/evaluation-materials/gold/sample_manifest.csv"
+    ).expanduser()
+    if not manifest.exists():
+        pytest.skip("gold sample not drawn in this environment")
+    frame = pd.read_csv(manifest)
+
+    assert frame["pmid"].notna().all()
+    assert frame["doc_uid"].notna().all()
+    assert (frame["doc_uid"].astype(str) == frame["pmid"].astype(str)).all()
+    assert not frame["pmid"].duplicated().any()
+    # mag is retained: it is the key the fit corpus uses for overlap checks.
+    assert frame["mag"].notna().all()
+
+
+# --------------------------------------------------------------- strata and draws
+
+
+def _pool() -> "pd.DataFrame":
+    import pandas as pd
+
+    rows = []
+    for i in range(60):
+        rows.append(
+            {
+                "doc_id": i,
+                "doc_uid": str(1000 + i),
+                "publication_year": 1995 + i % 30,
+                "n_verb_mentions": 0 if i < 12 else 1 + i % 5,
+                "labels_detected": ""
+                if i < 12
+                else ("rare" if i % 3 == 0 else "common"),
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def test_tail_labels_are_those_outside_the_top_k() -> None:
+    import pandas as pd
+
+    labels = pd.Series(["a|b", "a", "a|c", "b"])
+
+    assert sg.tail_labels(labels, top_k=2) == frozenset({"c"})
+
+
+def test_strata_split_density_and_tail() -> None:
+    pool = _pool()
+    strata = sg.assign_strata(pool, frozenset({"rare"}))
+
+    assert strata[pool["n_verb_mentions"] == 0].str.endswith("-none").all()
+    positive = strata[pool["n_verb_mentions"] > 0]
+    assert positive.str.endswith(("-tail", "-common")).all()
+    assert (strata[pool["labels_detected"] == "rare"].str.endswith("-tail")).all()
+
+
+def test_a_draw_records_inclusion_probabilities_and_the_negative_quota() -> None:
+    pool = _pool()
+    pool["stratum"] = sg.assign_strata(pool, frozenset({"rare"}))
+
+    drawn = sg.draw_batch(pool, n_total=20, none_fraction=0.1, tail_weight=2.0, seed=1)
+
+    assert len(drawn) == 20
+    assert (drawn["n_verb_mentions"] == 0).sum() == 2
+    sizes = pool.groupby("stratum").size()
+    for name, group in drawn.groupby("stratum"):
+        assert group["p_incl"].iloc[0] == len(group) / sizes[name]
+
+
+def test_tail_weight_over_allocates_tail_strata() -> None:
+    import pandas as pd
+
+    sizes = pd.Series({"a-tail": 10, "b-common": 10})
+    weights = pd.Series({"a-tail": 3.0, "b-common": 1.0})
+
+    alloc = sg.allocate(sizes, 8, weights)
+
+    assert alloc.sum() == 8
+    assert alloc["a-tail"] == 6 and alloc["b-common"] == 2
+
+
+def test_allocation_never_exceeds_a_stratum() -> None:
+    import pandas as pd
+
+    sizes = pd.Series({"a": 2, "b": 50})
+    alloc = sg.allocate(sizes, 10, pd.Series({"a": 10.0, "b": 1.0}))
+
+    assert alloc["a"] == 2 and alloc.sum() == 10
+
+
+def test_roles_follow_the_requested_proportions() -> None:
+    roles = sg.assign_roles(12, 4, 1, 1)
+
+    assert roles.count("primary") == 8
+    assert roles.count("double") == 2
+    assert roles.count("reserve") == 2
diff --git a/test/test_sample_quality.py b/test/test_sample_quality.py
new file mode 100644
index 0000000..6af3dc1
--- /dev/null
+++ b/test/test_sample_quality.py
@@ -0,0 +1,86 @@
+"""Eligibility gate for the gold sample: each rule, rule order, overlap and duplicates."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+
+from pelinker.eval import sample_quality as sq
+
+ABSTRACT = (
+    "Tumour-associated macrophages secrete IL-10 in the tumour microenvironment. "
+    "We show that this secretion is regulated by STAT3 and that it suppresses the "
+    "activity of cytotoxic T cells. Inhibition of STAT3 restored T-cell function in "
+    "two mouse models, and the effect was confirmed in samples from patients with "
+    "ovarian cancer. These results identify STAT3 as a target for combination therapy "
+    "and suggest that the balance of cytokines in the tumour shapes the response."
+)
+RULES = sq.Eligibility()
+
+
+def test_a_research_abstract_is_eligible() -> None:
+    assert sq.drop_reason(ABSTRACT, 2015, RULES) is None
+
+
+def test_each_rule_names_its_reason() -> None:
+    assert sq.drop_reason(ABSTRACT, 1985, RULES) == sq.REASON_YEAR
+    assert sq.drop_reason("J Pediatr. (2001). 15, 211.", 2015, RULES) == sq.REASON_SHORT
+    assert sq.drop_reason(ABSTRACT * 20, 2015, RULES) == sq.REASON_LONG
+    one_sentence = ABSTRACT.replace(". ", "; ")
+    assert sq.drop_reason(one_sentence, 2015, RULES) == sq.REASON_SENTENCES
+    french = (
+        "Il peut paraitre indecent de dire que la maladie a virus Ebola peut representer "
+        "une opportunite. Les systemes de sante sont fragiles. Une reforme est urgente. "
+    ) * 3
+    assert sq.drop_reason(french, 2015, RULES) == sq.REASON_LANGUAGE
+
+
+def test_an_unparsable_year_is_not_eligible() -> None:
+    assert sq.drop_reason(ABSTRACT, "n/a", RULES) == sq.REASON_YEAR
+
+
+def test_training_overlap_and_duplicates_are_table_level() -> None:
+    other = ABSTRACT.replace("IL-10", "IL-6")
+    df = pd.DataFrame(
+        {
+            "summary": [ABSTRACT, ABSTRACT, other, other],
+            "publication_year": [2015, 2015, 2015, 2015],
+            "mag": ["1", "2", "3", "4"],
+        }
+    )
+    training = sq.TrainingKeys(mag=frozenset({"3"}), text_sha1=frozenset())
+
+    reasons = sq.apply_gate(df, RULES, training=training)
+
+    assert reasons.tolist()[:3] == [None, sq.REASON_DUPLICATE, sq.REASON_TRAIN_OVERLAP]
+    # The text of row 3 also appears in row 4; the overlap is by mag, so row 4 stays.
+    assert reasons.iloc[3] is None
+
+
+def test_training_text_hash_catches_a_reissued_id() -> None:
+    df = pd.DataFrame({"summary": [ABSTRACT], "publication_year": [2015], "mag": ["9"]})
+    training = sq.TrainingKeys(
+        mag=frozenset(), text_sha1=frozenset({sq.text_sha1(ABSTRACT)})
+    )
+
+    assert (
+        sq.apply_gate(df, RULES, training=training).iloc[0] == sq.REASON_TRAIN_OVERLAP
+    )
+
+
+def test_load_training_keys_reads_id_and_text_columns(tmp_path: Path) -> None:
+    path = tmp_path / "fit.tsv"
+    path.write_text(f"0\t1\n42\t{ABSTRACT}\n", encoding="utf-8")
+
+    keys = sq.load_training_keys(path)
+
+    assert keys.mag == frozenset({"42"})
+    assert keys.text_sha1 == frozenset({sq.text_sha1(ABSTRACT)})
+
+
+def test_reason_counts_includes_eligible() -> None:
+    assert sq.reason_counts([None, sq.REASON_SHORT, None]) == {
+        "eligible": 2,
+        sq.REASON_SHORT: 1,
+    }
diff --git a/test/test_scale_curve_fit_integration.py b/test/test_scale_curve_fit_integration.py
index 124305c..7230016 100644
--- a/test/test_scale_curve_fit_integration.py
+++ b/test/test_scale_curve_fit_integration.py
@@ -8,7 +8,7 @@
 import pandas as pd
 import pytest
 
-from pelinker.config import (
+from pelinker.core.config import (
     EmbeddingModelMetadata,
     EmbeddingSourceSpec,
     LinkerFitConfig,
@@ -16,8 +16,8 @@
     TransformConfig,
 )
 from pelinker.model import Linker
-from pelinker.scale_curve import SCALE_CURVE_SCHEMA, load_scale_curve
-from pelinker.scaling import ScaleRung, fit_scale_curve
+from pelinker.search.scale_curve import SCALE_CURVE_SCHEMA, load_scale_curve
+from pelinker.core.scaling import ScaleRung, fit_scale_curve
 
 
 def _mentions_parquet(tmp_path: Path, n_ent: int = 24) -> tuple[Path, dict[str, str]]:
@@ -114,7 +114,7 @@ def test_without_a_curve_the_documented_default_is_used(tmp_path: Path) -> None:
 
 
 def test_provenance_reaches_the_fit_report(tmp_path: Path) -> None:
-    from pelinker.reporting import clustering_report_to_jsonable_dict
+    from pelinker.reports.io import clustering_report_to_jsonable_dict
 
     linker = _fit(tmp_path, min_cluster_size=None, scale_curve=_curve())
     report = linker.take_fit_clustering_report()
@@ -149,7 +149,7 @@ def test_scale_curve_json_round_trips_through_the_loader(tmp_path: Path) -> None
 def test_loader_rejects_an_unknown_schema(tmp_path: Path) -> None:
     path = tmp_path / "scale_curve.json"
     path.write_text(
-        json.dumps({"schema": "pelinker.scale_curve.v99", "curve": {}}),
+        json.dumps({"schema": "pelinker.search.scale_curve.v99", "curve": {}}),
         encoding="utf-8",
     )
 
diff --git a/test/test_scaling.py b/test/test_scaling.py
index 1bb2fb8..1d65a4b 100644
--- a/test/test_scaling.py
+++ b/test/test_scaling.py
@@ -6,7 +6,7 @@
 
 import pytest
 
-from pelinker.scaling import (
+from pelinker.core.scaling import (
     MIN_RUNGS_FOR_FIT,
     ScaleCurve,
     ScaleRung,
diff --git a/test/test_selected_hyperparameters.py b/test/test_selected_hyperparameters.py
index 2177987..bc68029 100644
--- a/test/test_selected_hyperparameters.py
+++ b/test/test_selected_hyperparameters.py
@@ -13,7 +13,7 @@
     FitCliConfig,
     _resolve_selection_hyperparameters,
 )
-from pelinker.selected_hyperparameters import (
+from pelinker.search.selected_hyperparameters import (
     SELECTED_HYPERPARAMETERS_BASENAME,
     SELECTED_HYPERPARAMETERS_SCHEMA,
     SelectedHyperparameters,
@@ -57,7 +57,7 @@ def test_handoff_round_trips_through_disk(tmp_path: Path) -> None:
 
 def test_handoff_rejects_an_unknown_schema(tmp_path: Path) -> None:
     p = tmp_path / SELECTED_HYPERPARAMETERS_BASENAME
-    p.write_text(json.dumps({"schema": "pelinker.selected_hyperparameters.v99"}))
+    p.write_text(json.dumps({"schema": "pelinker.search.selected_hyperparameters.v99"}))
 
     with pytest.raises(ValueError, match="expected schema"):
         load_selected_hyperparameters(p)
@@ -145,3 +145,43 @@ def test_matching_manifold_produces_no_warning(tmp_path: Path, caplog) -> None:
         )
 
     assert "different coordinates" not in caplog.text
+
+
+def test_the_class_view_round_trips_and_defaults_to_raw(tmp_path: Path) -> None:
+    write_selected_hyperparameters(_selected(class_view="reldir"), tmp_path)
+    assert load_selected_hyperparameters(tmp_path).class_view == "reldir"
+
+    legacy = _selected()
+    assert legacy.class_view is None
+    assert legacy.effective_class_view == "raw"
+
+
+def test_a_class_view_mismatch_between_search_and_fit_is_warned(
+    tmp_path: Path, caplog
+) -> None:
+    """MCS chosen against raw labels does not transfer to a reldir fit."""
+    import logging
+
+    write_selected_hyperparameters(_selected(manifold_kind="umap"), tmp_path)
+
+    with caplog.at_level(logging.WARNING, logger="pelinker.cli.fit"):
+        _resolve_selection_hyperparameters(
+            _cfg(selection_report=str(tmp_path), predict_mode="legacy")
+        )
+
+    assert "different reference partition" in caplog.text
+
+
+def test_a_matching_class_view_produces_no_warning(tmp_path: Path, caplog) -> None:
+    import logging
+
+    write_selected_hyperparameters(
+        _selected(manifold_kind="umap", class_view="reldir"), tmp_path
+    )
+
+    with caplog.at_level(logging.WARNING, logger="pelinker.cli.fit"):
+        _resolve_selection_hyperparameters(
+            _cfg(selection_report=str(tmp_path), predict_mode="legacy")
+        )
+
+    assert "different reference partition" not in caplog.text
diff --git a/test/test_selection_sampling.py b/test/test_selection_sampling.py
index b02f88e..5093cf0 100644
--- a/test/test_selection_sampling.py
+++ b/test/test_selection_sampling.py
@@ -5,9 +5,9 @@
 import numpy as np
 import pandas as pd
 
-from pelinker.config import ClusteringOptimizationConfig
-from pelinker.onto import NEGATIVE_LABEL
-from pelinker.sampling import (
+from pelinker.core.config import ClusteringOptimizationConfig
+from pelinker.core.onto import NEGATIVE_LABEL
+from pelinker.search.sampling import (
     cap_mentions_per_entity,
     draw_selection_sample,
     selection_sample_target_size,
diff --git a/test/test_text.py b/test/test_text.py
index 1e6d475..2acdcd4 100644
--- a/test/test_text.py
+++ b/test/test_text.py
@@ -1,4 +1,5 @@
-from pelinker.util import process_text, split_into_sentences
+from pelinker.text.embed import process_text
+from pelinker.text.tokenize import split_into_sentences
 
 
 def test_splitting(text):
diff --git a/test/test_util.py b/test/test_util.py
index 1b31ea0..a8caf94 100644
--- a/test/test_util.py
+++ b/test/test_util.py
@@ -1,23 +1,23 @@
 import torch
 
-from pelinker.onto import (
-    ChunkMapper,
+from pelinker.core.onto import (
     Expression,
     ExpressionHolder,
     ExpressionHolderBatch,
     NEGATIVE_LABEL,
     WordGrouping,
 )
-from pelinker.util import (
+from pelinker.text.chunking import ChunkMapper
+from pelinker.core.onto import SimplifiedToken
+from pelinker.text.chunking import split_text_into_batches
+from pelinker.text.embed import extract_and_embed_mentions
+from pelinker.text.tokenize import (
     get_word_boundaries,
     keep_expression_for_prediction,
     map_spans_to_spans_basic,
-    text_to_tokens,
     map_words_to_tokens,
-    split_text_into_batches,
+    text_to_tokens,
     token_list_with_window,
-    SimplifiedToken,
-    extract_and_embed_mentions,
 )
 
 
@@ -143,6 +143,31 @@ def test_map_spans_to_spans_basic_overlapping_sliding_windows():
     assert miti[(6, 18)] == [1, 2]
 
 
+def test_map_spans_to_spans_basic_matches_brute_force():
+    """The sorted-span fast path and the unsorted fallback agree with a full scan."""
+    import random
+
+    rng = random.Random(7)
+    for _ in range(200):
+        cuts = sorted(rng.sample(range(1, 80), 12))
+        tokens = [(a, b) for a, b in zip([0] + cuts, cuts)] + [(0, 0)] * 3
+        if rng.random() < 0.3:
+            rng.shuffle(tokens)
+        words = []
+        for _ in range(15):
+            a = rng.randrange(0, 80)
+            words.append((a, a + rng.randrange(1, 20)))
+        expected = {
+            w: [
+                j
+                for j, (ta, tb) in enumerate(tokens)
+                if tb > ta and ta < w[1] and tb > w[0]
+            ]
+            for w in words
+        }
+        assert map_spans_to_spans_basic(words, tokens) == expected
+
+
 class _FakeChunkMapper:
     @staticmethod
     def map_chunk_to_text(_itext, _ichunk):
@@ -154,6 +179,7 @@ def __init__(self, texts, by_wg):
         self.texts = texts
         self._by_wg = by_wg
         self.chunk_mapper = _FakeChunkMapper()
+        self.stokens_per_chunk = None  # no parse: every label is matched lexically
 
     def available_groupings(self):
         return list(self._by_wg.keys())
@@ -193,11 +219,11 @@ def _fake_report_for_negatives():
 
 def test_extract_mentions_negative_sampling_is_deterministic(monkeypatch):
     monkeypatch.setattr(
-        "pelinker.util.texts_to_vrep",
+        "pelinker.text.embed.texts_to_vrep",
         lambda *args, **kwargs: _fake_report_for_negatives(),
     )
     monkeypatch.setattr(
-        "pelinker.util.text_to_tokens",
+        "pelinker.text.embed.text_to_tokens",
         lambda nlp, text: [_st(0, text, lemma=text.lower())],
     )
     rows_a = extract_and_embed_mentions(
@@ -230,11 +256,11 @@ def test_extract_mentions_negative_sampling_is_deterministic(monkeypatch):
 
 def test_extract_mentions_negatives_are_global_not_per_entity(monkeypatch):
     monkeypatch.setattr(
-        "pelinker.util.texts_to_vrep",
+        "pelinker.text.embed.texts_to_vrep",
         lambda *args, **kwargs: _fake_report_for_negatives(),
     )
     monkeypatch.setattr(
-        "pelinker.util.text_to_tokens",
+        "pelinker.text.embed.text_to_tokens",
         lambda nlp, text: [_st(0, text, lemma=text.lower())],
     )
     rows = extract_and_embed_mentions(
diff --git a/test/test_vrep.py b/test/test_vrep.py
index 2648aa8..0693278 100644
--- a/test/test_vrep.py
+++ b/test/test_vrep.py
@@ -1,5 +1,5 @@
-from pelinker.util import texts_to_vrep
-from pelinker.onto import WordGrouping, MAX_LENGTH
+from pelinker.text.embed import texts_to_vrep
+from pelinker.core.onto import MAX_LENGTH, WordGrouping
 
 
 def test_vrep_word_groupings(tokenizer_model_scibert, nlp):
diff --git a/test/test_weak_label_check.py b/test/test_weak_label_check.py
new file mode 100644
index 0000000..60c1931
--- /dev/null
+++ b/test/test_weak_label_check.py
@@ -0,0 +1,115 @@
+"""Weak-label acceptance check: each violation is detected, a clean frame passes."""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+_MODULE_PATH = (
+    Path(__file__).resolve().parents[1] / "run" / "analysis" / "weak_label_check.py"
+)
+
+
+def _load_module():
+    spec = importlib.util.spec_from_file_location("weak_label_check", _MODULE_PATH)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+wl = _load_module()
+
+KB = pd.DataFrame(
+    {
+        "label": ["activates", "activated by", "interacts with"],
+        "canonical_entity_id": ["PEL.0", "PEL.0", "RO.1"],
+        "is_symmetric": [False, False, True],
+    }
+)
+
+
+def _rows(*records: tuple) -> pd.DataFrame:
+    return pd.DataFrame(
+        records,
+        columns=["pmid", "entity", "a_abs", "b_abs", "direction", "surface_rule"],
+    )
+
+
+def test_a_clean_frame_has_no_violations() -> None:
+    rows = _rows(
+        ("p1", "activates", 0, 9, "forward", "active"),
+        ("p1", "activated by", 20, 29, "forward", "passive_agent"),
+        ("p2", "interacts with", 3, 12, "symmetric", "active"),
+    )
+
+    assert set(wl.check(rows, KB)["violations"].values()) == {0}
+
+
+def test_a_relation_and_its_converse_on_one_span_are_caught() -> None:
+    rows = _rows(
+        ("p1", "activates", 0, 9, "forward", "lexical"),
+        ("p1", "activated by", 0, 9, "forward", "passive_agent"),
+    )
+
+    violations = wl.check(rows, KB)["violations"]
+
+    assert violations["multi_label_spans"] == 1
+    assert violations["converse_collisions"] == 1
+
+
+def test_nested_sites_and_flipped_symmetric_are_caught() -> None:
+    rows = _rows(
+        ("p1", "activates", 0, 9, "forward", "active"),
+        ("p1", "activated by", 0, 12, "forward", "passive_agent"),
+        ("p2", "interacts with", 3, 12, "inverse", "active"),
+    )
+
+    violations = wl.check(rows, KB)["violations"]
+
+    assert violations["nested_spans"] == 1
+    assert violations["symmetric_with_inverse_direction"] == 1
+
+
+def test_a_label_outside_the_kb_is_a_violation() -> None:
+    rows = _rows(("p1", "binds", 0, 5, "forward", "active"))
+
+    report = wl.check(rows, KB)
+
+    assert report["violations"]["labels_not_in_kb"] == 1
+    assert report["labels_not_in_kb"] == ["binds"]
+
+
+def test_views_count_classes_consistently_across_converse_coverage() -> None:
+    """A passive of a relation with a converse entry and a passive of one without it
+    are both an inverse class in reldir; raw keeps them as the labels they carry."""
+    kb = pd.DataFrame(
+        {
+            "entity_id": ["PEL.0", "PEL.1", "PEL.2"],
+            "label": ["activates", "activated by", "inhibits"],
+            "is_canonical": [True, False, True],
+            "canonical_entity_id": ["PEL.0", "PEL.0", "PEL.2"],
+            "is_symmetric": [False, False, False],
+        }
+    )
+    rows = _rows(
+        ("p1", "activates", 0, 9, "forward", "active"),
+        ("p1", "activated by", 20, 29, "forward", "passive_agent"),
+        ("p2", "inhibits", 3, 12, "inverse", "passive"),
+    )
+
+    views = wl.check(rows, kb)["views"]
+
+    assert views["classes_raw"] == 3
+    assert views["classes_rel"] == 2
+    assert views["classes_reldir"] == 3
+    assert views["relation_direction_mass"] == {"inverse": 2, "forward": 1}
+
+
+def test_views_are_skipped_without_a_pairs_kb() -> None:
+    rows = _rows(("p1", "activates", 0, 9, "forward", "active"))
+    assert wl.check(rows, KB)["views"] is None
diff --git a/test/test_writer.py b/test/test_writer.py
index 4714f9e..a02cf6c 100644
--- a/test/test_writer.py
+++ b/test/test_writer.py
@@ -44,7 +44,7 @@ def read_parquet_exact_chunks(path: Path, chunk_rows: int) -> Iterator[pd.DataFr
 import pyarrow as pa
 import pyarrow.parquet as pq
 import logging
-from pelinker.io.parquet import ParquetWriter
+from pelinker.data.parquet import ParquetWriter
 
 # Set up logging for tests
 logging.basicConfig(level=logging.INFO)
@@ -144,7 +144,7 @@ def test_basic_write_record_batch(temp_dir, sample_data):
     # Verify content
     table = read_parquet_file(output_path)
     assert table.num_rows == 3
-    assert table.num_columns == 10
+    assert table.num_columns == 12  # + direction, surface_rule
 
     # Check column names
     expected_columns = [
@@ -158,6 +158,8 @@ def test_basic_write_record_batch(temp_dir, sample_data):
         "itext",
         "ichunk",
         "embed",
+        "direction",
+        "surface_rule",
     ]
     assert table.column_names == expected_columns
 
@@ -188,7 +190,7 @@ def test_basic_write_table(temp_dir, sample_data):
     assert output_path.exists()
     table = read_parquet_file(output_path)
     assert table.num_rows == 3
-    assert table.num_columns == 10
+    assert table.num_columns == 12  # + direction, surface_rule
 
 
 def test_multiple_batches(temp_dir, sample_data):
diff --git a/uv.lock b/uv.lock
index 3588539..469ffd6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,3 +1,9 @@
+conflicts = [
+  [
+    {extra = "gpu-cu12", package = "pelinker"},
+    {extra = "gpu-cu13", package = "pelinker"}
+  ]
+]
 requires-python = ">=3.10.6"
 resolution-markers = [
   "python_full_version < '3.11'",
@@ -47,6 +53,24 @@ wheels = [
   {hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z", url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl"}
 ]
 
+[[package]]
+dependencies = [
+  {name = "anyio"},
+  {name = "docstring-parser"},
+  {name = "httpx2"},
+  {name = "jiter"},
+  {name = "pydantic"},
+  {name = "sniffio"},
+  {name = "typing-extensions"}
+]
+name = "anthropic"
+sdist = {hash = "sha256:3906fabac7ad7b5b46c6186040398fc7826885c77ce34e4dd7849de16fc8d0f8", size = 1390390, upload-time = "2026-09-30T22:58:21.098Z", url = "https://files.pythonhosted.org/packages/ad/12/9a6ffa397b172adb040008d934a1dfd85d0e4cefc77489416db294ffc880/anthropic-1.11.0.tar.gz"}
+source = {registry = "https://pypi.org/simple"}
+version = "1.11.0"
+wheels = [
+  {hash = "sha256:52f97b2c485cca7ac66058374f5073a3febb7e6849b16989c145d602a3efee21", size = 1681654, upload-time = "2026-09-30T22:58:22.925Z", url = "https://files.pythonhosted.org/packages/ab/6f/20edcd2956cac68042b1bcdd13ab3311447519f86b26929ff268dc74c650/anthropic-1.11.0-py3-none-any.whl"}
+]
+
 [[package]]
 name = "antlr4-python3-runtime"
 sdist = {hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z", url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz"}
@@ -55,66 +79,25 @@ version = "4.9.3"
 
 [[package]]
 dependencies = [
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   {name = "click"},
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   {name = "fastapi"},
   {name = "hdbscan"},
   {name = "hydra-core"},
   {name = "joblib"},
-  {name = "pip"},
+  {name = "plotly"},
   {name = "pyarrow"},
+  {name = "pysankey"},
   {name = "rich"},
   {name = "scikit-optimize"},
+  {name = "seaborn"},
   {name = "sentence-transformers"},
   {name = "spacy"},
   {name = "tf-keras"},
-  {name = "torch"},
   {name = "transformers"},
   {name = "uvicorn"},
   {name = "wordfreq"}
@@ -3517,17 +5135,17 @@ source = {editable = "."}
 version = "0.2.0"
 
 [package.metadata]
-provides-extras = ["dev", "docs", "gpu"]
+provides-extras = ["dev", "docs", "eval", "gpu-cu12", "gpu-cu13", "preprocess"]
 requires-dist = [
+  {conflict = {extra = "gpu-cu12", package = "pelinker"}, index = "https://download.pytorch.org/whl/cu126", marker = "extra == 'gpu-cu12'", name = "torch", specifier = ">=2.13.0"},
+  {extras = ["ctk"], marker = "extra == 'gpu-cu12'", name = "cupy-cuda12x", specifier = ">=14.2.0"},
+  {extras = ["ctk"], marker = "extra == 'gpu-cu13'", name = "cupy-cuda13x", specifier = ">=14.2.0"},
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-  {marker = "extra == 'dev'", name = "mypy", specifier = ">=1.11"},
-  {marker = "extra == 'dev'", name = "plotly", specifier = ">=6.2.0,<7"},
+  {marker = "extra == 'dev'", name = "en-core-web-lg", url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.8.0/en_core_web_lg-3.8.0-py3-none-any.whl"},
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-  {marker = "extra == 'dev'", name = "seaborn", specifier = ">=0.13.2,<0.14"},
+  {marker = "extra == 'dev'", name = "ty", specifier = ">=0.0.1a10"},
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   {marker = "extra == 'docs'", name = "mkdocs", specifier = ">=1.6.1"},
   {marker = "extra == 'docs'", name = "mkdocs-gen-files", specifier = ">=0.5.0"},
@@ -3536,38 +5154,41 @@ requires-dist = [
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-  {marker = "extra == 'gpu'", name = "cupy-cuda12x", specifier = ">=13.6.0"},
+  {marker = "extra == 'eval'", name = "anthropic", specifier = ">=0.40"},
+  {marker = "extra == 'eval'", name = "google-genai", specifier = ">=1.0"},
+  {marker = "extra == 'eval'", name = "openai", specifier = ">=1.60"},
+  {marker = "extra == 'gpu-cu13'", name = "torch", specifier = ">=2.13.0"},
+  {marker = "extra == 'preprocess'", name = "rdflib", specifier = ">=7.0"},
   {name = "click", specifier = ">=8.1,<9"},
-  {name = "cupy-cuda12x", specifier = ">=13.6.0"},
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   {name = "hydra-core", specifier = ">=1.3.2"},
   {name = "joblib", specifier = ">=1.4.2,<2"},
+  {name = "matplotlib", specifier = ">=3.9"},
   {name = "pandas", specifier = ">=2.3.3"},
-  {name = "pip", specifier = ">=26.0.1"},
+  {name = "plotly", specifier = ">=6.2.0,<7"},
   {name = "pyarrow", specifier = ">=23.0.1"},
+  {name = "pysankey", specifier = ">=0.0.1"},
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   {name = "scikit-learn", specifier = ">=1.5.0,<2"},
   {name = "scikit-optimize", specifier = ">=0.10.2"},
-  {name = "sentence-transformers", specifier = ">=3.0.1,<4"},
+  {name = "seaborn", specifier = ">=0.13.2,<0.14"},
+  {name = "sentence-transformers", specifier = ">=5.6,<6"},
   {name = "spacy", specifier = ">=3.7.5,<4"},
   {name = "tf-keras", specifier = ">=2.16.0"},
-  {name = "torch", specifier = ">=2.9.1"},
-  {name = "transformers", specifier = ">=4.41.2,<5"},
+  {name = "torch", specifier = ">=2.13.0"},
+  {name = "transformers", specifier = ">=5.5.0,<6"},
   {name = "uvicorn", specifier = ">=0.42.0"},
   {name = "wordfreq", specifier = ">=3.1.1"}
 ]
 
 [package.optional-dependencies]
 dev = [
-  {name = "bumplot"},
-  {name = "mypy"},
-  {name = "plotly"},
+  {name = "en-core-web-lg"},
   {name = "pre-commit"},
-  {name = "pysankey"},
   {name = "pytest"},
-  {name = "seaborn"}
+  {name = "ty"}
 ]
 docs = [
   {extra = ["python"], name = "mkdocstrings"},
@@ -3580,13 +5201,26 @@ docs = [
   {name = "mkdocs-material"},
   {name = "mkdocstrings-python"}
 ]
-gpu = [
-  {name = "cupy-cuda12x"}
+eval = [
+  {name = "anthropic"},
+  {name = "google-genai"},
+  {name = "openai"}
+]
+gpu-cu12 = [
+  {extra = ["ctk"], marker = "extra == 'extra-8-pelinker-gpu-cu12'", name = "cupy-cuda12x"},
+  {name = "torch", source = {registry = "https://download.pytorch.org/whl/cu126"}, version = "2.14.1+cu126"}
+]
+gpu-cu13 = [
+  {extra = ["ctk"], marker = "extra == 'extra-8-pelinker-gpu-cu13'", name = "cupy-cuda13x"},
+  {name = "torch", source = {registry = "https://pypi.org/simple"}, version = "2.14.1"}
+]
+preprocess = [
+  {name = "rdflib"}
 ]
 
 [[package]]
 dependencies = [
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+  {marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32') or (sys_platform == 'emscripten' and extra == 'extra-8-pelinker-gpu-cu12' and extra == 'extra-8-pelinker-gpu-cu13') or (sys_platform == 'win32' and extra == 'extra-8-pelinker-gpu-cu12' and extra == 'extra-8-pelinker-gpu-cu13')", name = "ptyprocess"}
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 [[package]]
@@ -3820,11 +5445,12 @@ wheels = [
 
 [[package]]
 dependencies = [
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+  {marker = "python_full_version < '3.11' or (extra == 'extra-8-pelinker-gpu-cu12' and extra == 'extra-8-pelinker-gpu-cu13')", name = "markdown", source = {registry = "https://pypi.org/simple"}, version = "3.10.3"},
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+  {marker = "sys_platform == 'win32' or (extra == 'extra-8-pelinker-gpu-cu12' and extra == 'extra-8-pelinker-gpu-cu13')", name = "colorama"},
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@@ -3843,17 +5469,17 @@ wheels = [
 
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@@ -6029,11 +8078,11 @@ dependencies = [
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