feat: arche-synthetic supplier world

data/synthetic/.gitignore

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+__pycache__/
+*.py[cod]
+.venv/
+dist/
+build/
+*.egg-info/
+.pytest_cache/
+.ruff_cache/

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data/synthetic/README.md

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+# arche-synthetic
+
+Known entity worlds, imperfect observations, exact ground truth — and every disagreement labelled with **why it happened**.
+
+```bash
+pip install arche-synthetic          # pyarrow and pyyaml. Nothing else.
+python -m arche_synthetic --scale 400 --out ./worlds/ng
+```
+
+```python
+from arche_synthetic import Benchmark, Predictions, build
+
+world, manifest = build("ng_supplier_v0", seed=42, out=Path("worlds/ng"))
+
+bench = Benchmark.load(Path("worlds/ng"))
+report = bench.score(Predictions(arm="mine", pairs=my_predicted_pairs))
+report["recall_by_stratum"]["change/ORG_RELOCATED"]   # the column nobody else has
+```
+
+From a checkout of this repository, run the scripts **from the repo root**. `data/synthetic` has its own `pyproject.toml`, so `uv run` with the working directory inside it resolves to the standalone project and will not find arche:
+
+```bash
+python data/synthetic/build_ng_supplier_v0.py          # ~7s, writes worlds/ng_supplier_v0/
+python data/synthetic/run_benchmark.py --world data/synthetic/worlds/ng_supplier_v0_s150
+```
+
+## A second world: artists, for the variant-list question
+
+`artists_v0` is 9,944 records of 2,941 real African musicians under their real Wikidata aliases, built to answer one question put to us by Splink's author: what is a name-variant list worth to a matcher? The world, the independent MusicBrainz-derived list, a ninety-line Splink example and the full six-arm result are in [`datasets/artists_dataops/`](../../datasets/artists_dataops/README.md).
+
+```bash
+python -m arche_synthetic --world-pack artists_v0 --scale 10000 --out data/synthetic/worlds/artists_v0
+```
+
+## Why this exists
+
+arche keeps being blocked by data licensing. ai4privacy's 300k is academic-only and forbids derivatives; i2b2 needs a data-use agreement; OpenSanctions Pairs is a purchase. Every one of those is somebody else's permission. This is the path that needs nobody's.
+
+And one thing arche can do had never been measured: `ledger.observe()` — add evidence about a record, re-decide its open pairs, supersede rather than overwrite — had no benchmark, because measuring it needs data where an entity *legitimately changed* and the truth says so. No public entity-resolution benchmark has that column. This generates it, and [RESULTS.md](RESULTS.md) is what it found.
+
+## The idea in one table
+
+| kind | example | are both records true? |
+|---|---|---|
+| `change` | the supplier moved; the director resigned; the account was replaced | **yes**, at their respective times |
+| `representation` | `Établissements Koné & Fils SARL` → `ETS Kone & Fils` | yes — same fact, different encoding |
+| `error` | typo, OCR misread, truncation, mutated digit | no — one is a distortion |
+
+Existing synthetic ER benchmarks produce only the third row: take a record, corrupt it, call the result a duplicate. A matcher tuned on that learns that all disagreement means "different entity", and then splits entities in production the first time a supplier moves.
+
+The labels are computed **by construction**. Every observed value carries a note of which state it came from and which transforms were applied, so a difference is explained by comparing provenance rather than by guessing from the strings.
+
+## The modules
+
+```
+arche_synthetic/
+├── ids.py         opaque record ids — the invariant that keeps truth out of the input
+├── world.py       entities, states, relationships; the frequency-weighted name draw
+├── lifecycle.py   events: relocation, rename, director change, account change
+├── observe.py     sources, representation rules, corruption rules, difference labelling
+├── export.py      Parquet + JSON Schema + manifest, and the write-time leak check
+├── evaluate.py    the stratified scorer — recall stratifies, precision does not
+└── data/          the vendored name lexicon (CC-BY-4.0)
+```
+
+**Nothing imports `arche` at all.** The name lexicon is vendored (from arche's `african_names_v1`, itself built from Wikidata/ParaNames), and the RC/TIN format checks are local — with `test_synthetic_world.py::test_the_local_validators_agree_with_arches` asserting they still agree with arche's, which is stronger than importing either, because a one-sided import would hide a drift.
+
+Verified rather than asserted: installed into an empty virtualenv with only `pyarrow` and `pyyaml` beside it, the package generates a world whose content fingerprints are **identical** to one built inside this repo.
+
+## Is it realistic enough to measure on?
+
+`examples/notebooks/24_is_the_synthetic_world_realistic.ipynb` holds this world up against **51,023 real Nigerian facility names** (GRID3) and tests three hypotheses stated in advance. Summary: the collision *rate* is realistic (13.5% against 12.0%), the vocabulary is 2.8x thinner at matched sample size, and **0.0% of generated name tokens carry regional signal against 90.4% of real ones** — the correlation defect, measured rather than asserted. Which published numbers that does and does not affect is the notebook's last cell.
+
+## Output
+
+See [DATACARD.md](DATACARD.md) for what the shipped world contains, every measured number, and — as importantly — what it is not.
+
+**The contract is the files, not a Python class.** Parquet with documented columns, a JSON Schema beside each one, a manifest with provenance. A Splink, Zingg, R or SQL user consumes it without installing anything of ours. The moment the truth is only readable through our own types, matcher-neutrality is gone.
+
+## Running the benchmark
+
+```bash
+python data/synthetic/run_benchmark.py --world data/synthetic/worlds/ng_supplier_v0_s150
+python data/synthetic/run_benchmark.py --arms exact,jaro_winkler     # the fast ones
+```
+
+Seven arms: `exact` (normalised name + city join), `jaro_winkler`, `splink` (Fellegi-Sunter, its own decision boundary), `arche/pack`, `arche/custom`, and the two ledger arms — `arche/ledger` (records ingested in arrival order) and `arche/ledger+observe` (plus the operator's registry lookup). **Measured results and their caveats: [RESULTS.md](RESULTS.md).** The short version — every engine that reads the address fails on relocated suppliers, and the ledger path is the only thing that moves the number.
+
+The arms are extras, so the base install stays two dependencies: `pip install "arche-synthetic[bench]"` for the baselines, `[splink]` for the Splink arm, `[arche]` for the arche and ledger arms, `[all]` for everything.
+
+### Cutting the repository
+
+The trigger the plan set was *a stratified result **and** arche not being the only arm*. Both hold: Splink runs on this data and lands within 0.007 recall of `arche/pack`.
+
+This directory is now a complete standalone project — `pyproject.toml`, `LICENSE`, `.gitignore`, a vendored lexicon, no arche import anywhere, installable and verified in a clean environment. What remains is one command to extract its history and one to publish it:
+
+```bash
+git subtree split -P data/synthetic -b arche-synthetic
+```
+
+then push that branch to a newly created `unpatterned-labs/arche-synthetic` and set `main` to it.
+
+**Publishing is the owner's to do.** Nothing in this branch creates a remote, pushes, or uploads anything; the repository's guards block an agent from doing so, and that is the correct arrangement.
+
+Afterwards, in this repository: drop the directory, add `arche-synthetic` as a dev dependency, and keep a thin consumer that runs the arche arms against the published package. The three test files in `packages/arche-core/tests/` move last — they become the new repo's `tests/`, and until the cut they are what keeps the generator honest inside arche's CI.