---
license: cc-by-4.0
language:
- en
source_datasets:
- princeton-wordnet-3.0
- opengloss-v1.3
size_categories:
- 100K<n<1M
task_categories:
- token-classification
- text-classification
tags:
- opengloss
- synthetic
- lexicography
- english
- morphology
- inflection
- lemmatization
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
---

> **Superseded by [OpenGloss v2.3](https://huggingface.co/datasets/mjbommar/opengloss-v2.3-inflections)** (2026-09-09): tier 6 adds ~12,000 named entities (people, places, organizations, works, events) with `entity_type`, Wikidata ids and `alias_of` links, and every proper noun in the release is now typed. v2.2 stays published for reproducibility.

# OpenGloss v2.2 — Inflections

A flat form→lemma lookup table, one row per surface string a consumer might actually type or scan: every stored inflected form (`plural`, `past_tense`, `past_participle`, `present_participle`, `third_person_singular`, `comparative`, `superlative`), every recorded `derivation`, and — critically — one `lemma` row for the headword itself, so resolving *any* surface string, inflected or not, is the same one lookup rather than a branch on whether stemming is needed first. Sourced straight from each POS entry's `morphology`, the same structure `opengloss-v2.2-lexicon` carries nested (D-75).

Part of the **OpenGloss v2.2** release family — 16 datasets built from one
store of 148,292 lexemes and 288,304 live senses, all joinable
on derived ids. See [Related datasets](#related-datasets) for the rest.

## What's new in v2.2 vs v1.3

1. **Schema v3.** Every lexeme carries a `kind` discriminator (simplex, compound, phrasal
   verb, idiom, proper noun, abbreviation, affix, function word); every sense carries a
   controlled domain leaf from a fixed ~160-leaf taxonomy instead of free text; every
   example carries the character span of the headword occurrence inside it.
2. **Renditions, not one string.** A definition is a *set*: the canonical one plus
   rewrites at four reading levels and in four registers, each produced in a single call
   from the canonical text so they say the same thing at different altitudes.
3. **A sense graph, not a word graph.** Typed relations resolve to *sense* ids wherever
   the target's entry exists in the release, so `bank --hypernym--> financial institution`
   points at a meaning rather than at a string.
4. **Retrieval data is first-class.** Synthetic per-sense queries in eight styles,
   grounded QA pairs, mined word-in-context pairs, MS MARCO-style triples with
   graph-derived hard negatives, and graded TREC qrels — all derivable from, and
   consistent with, the same entries.
5. **Derivable identifiers everywhere.** v1.3 published a positional id for lexemes and
   senses (`3d_model_noun_0`) and nothing below that. v2.2 gives every rendition, edge,
   query, QA pair and provenance record an id computable from the row alone, and never
   renumbers: a retired sense is tombstoned, so the ids after it keep their meaning.
6. **Per-field provenance.** Which model wrote a field, how many tokens it took, what it
   cost — published as its own dataset.

## What changed since v2.1

v2.1 (2026-09-07) added tier 4 and the `inflections` repo. v2.2 adds **tier 5**:
43,652 WordNet 3.0 candidate lemmas the earlier tiers lacked — common
compounds and technical nouns, adjectives, adverbs and verbs, instances/taxa/organisms
excluded — 38,526 of them imported outright, the rest matched against
v1.3's own files. The other three changes are about honesty rather than coverage:

- **The lemma fold.** `lexeme-hygiene` (D-79) folded 4,377
  inflected-form headwords onto the lemma that already carried their meaning
  ("databases" onto "database", through the store's own recorded morphology) and
  retired 172 multiword fragments that began or ended on a
  function word ("is not", "on top of"). Together with D-76's phantom part-of-speech
  retirements, 4,549 lexemes store-wide now have every sense
  tombstoned. A lexeme like that is **not counted as a lexeme** anywhere in this card or
  in `Stats` any more — it has no live sense, so it is not a lexeme by this release's own
  count — but it is not gone: its surface form still resolves through
  `opengloss-v2.2-inflections`, and its `lexicon` row carries `retired = true` with a
  `retired_reason` explaining why.
- **Provenance on inherited fields.** Every field a migration or import wrote, not only
  what a model wrote from scratch, now carries a `migrate`-stage provenance record
  naming where it came from, so "where did this text come from" is answerable by
  `grep` rather than by trusting the pipeline that happened to run.
- **A `source` column** on `lexicon` and `senses`: `opengloss-v1.3` for content this
  project generated or migrated from its own legacy releases, `wordnet-3.0` for the
  tier-5 entries imported directly from Princeton WordNet 3.0.

|  | v2.1 (2026-09-07) | v2.2 |
|---|---|---|
| Lexemes | 109,633 | 148,292 |
| Live senses | 250,003 | 288,304 |
| Tier 5 lexemes (WordNet gap) | 0 | 43,226 |
| Retired lexemes (every sense tombstoned) | 0 | 4,567 |
| Pretraining documents | 1,111,044 | 1,458,684 |
| Pretraining words | 331,888,239 | 398,029,628 |
| Pretraining tokens (cl100k_base) | 471,451,693 | 565,384,746 |
| Judge score, Opus, 40-entry samples | 70.2 (core + tier 2), 66.7 (tier 3), 67.0 (tier 4) | 70.2 (core + tier 2), 66.7 (tier 3), 67.0 (tier 4), 81.3 (tier 5) |

**Schema.** No column was removed or retyped. `lexicon` gains `source`, `retired` and
`retired_reason`; `senses` gains `source`; `tier` gains the value `tier5`.

## What changed since v2.0

v2.0 (2026-09-05) covered the frequency-ranked single words. v2.2 adds **tier 4**: the
function words the core ranking had excluded on purpose, and every remaining v1.3 entry
at Wikipedia frequency ≥ 10 — mostly multiword compounds ("natural selection",
"catalog number"), plus names and rarer single words. That doubles the lexeme count and
changes the mix: v2.0 was 99.8% single words; a third of v2.2 is multiword.

|  | v2.0 (2026-09-05) | v2.1 (2026-09-07) |
|---|---|---|
| Lexemes | 54,724 | 109,633 |
| Live senses | 137,314 | 250,003 |
| Multiword entries (compounds, phrasal verbs, idioms) | 86 | 36,366 |
| Proper nouns | 10,365 | 17,073 |
| Function words | 114 | 462 |
| Gloss renditions | 1,129,975 | 1,684,865 |
| Example sentences | 1,398,297 | 2,163,329 |
| Live relations | 735,318 | 1,574,438 |
| Synthetic queries | 1,330,311 | 1,304,650 |
| QA pairs | 750,348 | 736,010 |
| Pretraining documents | 617,175 | 1,111,044 |
| Pretraining words | 196,390,946 | 331,888,239 |
| Pretraining tokens (cl100k_base) | 275,659,096 | 471,451,693 |
| Judge score, Opus, 40-entry samples | 70.2 (core + tier 2), 66.7 (tier 3) | 70.2 (core + tier 2), 66.7 (tier 3), 67.0 (tier 4) |

**Schema.** No column was added, removed or retyped in any existing dataset. Three
things did change:

- `tier` gains the value `tier4` (it was `core`, `tier2` or `tier3`).
- One new dataset, `opengloss-v2.2-inflections`: a flat surface-form → lemma lookup
  (plural, past tense, participles, comparative, superlative, derivations) built from
  the morphology that the lexicon already carried nested.
- New provenance note prefixes on tombstones and edges, all reversible and all counted
  in the store audit: `phantom_pos:` (a v1.3 part-of-speech block whose glosses defined
  a component word rather than the compound — 11,440 blocks retired), `regen:`
  (relations regenerated for senses that had lost every edge to judging), and
  `retyped: contrast` (synonym edges the contrast paragraphs showed to be hypernym or
  hyponym).

**Not row-compatible with v2.0.** Lexeme, sense, rendition, edge, query and QA ids are
stable for every entry v2.0 had. The derived training sets (`retrieval-pairs`,
`retrieval-triples`, `qrels`) re-sample negatives over the larger pool, so their rows
differ; and the store-wide quality passes run for v2.2 retired ~3,000 senses of the v2.0
entries (phantom part-of-speech blocks and near-duplicate senses), so those senses are
now tombstoned rather than live. Treat v2.2 as a new release, not a delta.

### Scope: fewer headwords, far more per headword

v2.2 is **not** a superset of v1.3. It covers 148,292 of v1.3's 205,988
lexemes — every frequency-ranked single word, plus the compounds and names at Wikipedia
frequency ≥ 10 — and spends the difference on depth.
If you need breadth of vocabulary, use
[v1.3](https://huggingface.co/datasets/mjbommar/opengloss-v1.3-definitions); if you need graded renditions, resolved
relations, spans, or retrieval supervision, use v2.2.

|  | v1.3 | v2.2 |
|---|---|---|
| Lexemes | 205,988 | 148,292 |
| Senses | 565,604 | 288,304 |
| Definition renditions per sense | 1 canonical | 1 canonical + up to 8 graded |
| Relation targets | bare strings | resolved to sense ids |
| Retrieval training data | companion sets | queries, QA, triples, qrels |
| Per-field provenance | no | model, tokens and cost per call |

## Key statistics

|  |  |
|---|---|
| Lexemes | 148,292 |
| Retired lexemes (every sense tombstoned; not counted above) | 4,567 |
| Live senses | 288,304 |
| Rows in this dataset | 867,956 |
| Forms | 867,956 |
| Lemma rows | 198,615 |
| Rows per lexeme (mean) | 5.9 |

### By tier

- `core` — top 10K by composite frequency
- `tier2` — ranks to ~42K
- `tier3` — the rest of the frequency-ranked single words
- `tier4` — stopwords, plus compounds and names at Wikipedia frequency ≥ 10
- `tier5` — the WordNet 3.0 lemmas the earlier tiers lacked: common compounds and technical nouns, adjectives, adverbs and verbs (instances, taxa and organisms excluded); 5,126 from v1.3 files, the rest imported from WordNet

| Tier | Lexemes | Live senses |
|---|---|---|
| `core` | 9,427 | 32,193 |
| `tier2` | 30,346 | 71,957 |
| `tier3` | 11,452 | 23,511 |
| `tier4` | 53,841 | 113,873 |
| `tier5` | 43,226 | 46,770 |

### Coverage by tier

The release was built in 5 frequency-ranked passes (`core`, `tier2`, `tier3`, `tier4` and `tier5`) and they did not all receive the same stages. This table is per-field and per-tier so the gaps are visible rather
than averaged away.

| Field | Of | `core` | `tier2` | `tier3` | `tier4` | `tier5` |
|---|---|---|---|---|---|---|
| Canonical gloss | sense | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
| Controlled domain tag | sense | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
| Gloss at 4 reading levels | sense | 100.0% | 99.9% | 99.9% | 100.0% | 99.9% |
| Gloss in 4 registers | sense | 100.0% | 100.0% | 0.0% | 0.0% | 0.0% |
| At least one example | sense | 100.0% | 99.9% | 99.8% | 98.4% | 100.0% |
| Examples at 4 reading levels | sense | 99.0% | 99.6% | 99.7% | 97.6% | 100.0% |
| At least one relation | sense | 99.3% | 99.2% | 99.2% | 99.2% | 94.4% |
| Synthetic retrieval queries | sense | 100.0% | 100.0% | 0.0% | 0.0% | 0.0% |
| Grounded QA pairs | sense | 99.8% | 99.6% | 0.0% | 0.0% | 0.0% |
| Etymology | lexeme | 100.0% | 100.0% | 99.8% | 100.0% | 100.0% |
| Lexical explanation | lexeme | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
| Encyclopedia (neutral) | lexeme | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
| Encyclopedia at grade 5 + college (core entries also carry grade 1 and grade 10) | lexeme | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
| Contrast paragraphs | lexeme | 74.9% | 56.2% | 0.0% | 0.0% | 0.0% |

### By relation

| Relation | Rows |
|---|---|
| `derivation` | 420,977 |
| `lemma` | 198,615 |
| `plural` | 88,919 |
| `comparative` | 34,788 |
| `superlative` | 34,779 |
| `present_participle` | 23,842 |
| `past_tense` | 22,293 |
| `third_person_singular` | 22,263 |
| `past_participle` | 21,480 |

### Files

| Files | Config | Rows | Shards | Size |
|---|---|---|---|---|
| `data/train-*.parquet` | default | 867,956 | 2 | 11.4 MB |

## Fields

867,956 rows, one row per inflected, derived or lemma form.

| Field | Type | Description |
|---|---|---|
| `form` | `string` | The surface form, case preserved as stored. |
| `form_normalized` | `string` | `form.lower()` — filter on this column when the input's casing is not known to match. |
| `lexeme_id` | `string` | Entry id: `slugify(headword)`. Join key across the family. |
| `headword` | `string` | The entry's surface headword. |
| `tier` | `string` | `core` (top 10K by composite frequency), `tier2` (ranks to ~42K), `tier3` (the rest of the frequency-ranked single words), `tier4` (stopwords, plus compounds and names at Wikipedia frequency ≥ 10), `tier5` (the WordNet 3.0 gap the earlier tiers lacked) or `unknown` (on none of the rank lists); an export may contain only some of these — see the coverage table. |
| `pos` | `string` | Part of speech of the owning POS entry. |
| `relation` | `string` | `lemma` (the headword itself), `plural`, `past_tense`, `past_participle`, `present_participle`, `third_person_singular`, `comparative`, `superlative` or `derivation`. |

**One real row:**

```json
{
  "form": "0",
  "form_normalized": "0",
  "lexeme_id": "0",
  "headword": "0",
  "pos": "noun",
  "tier": "tier5",
  "relation": "lemma"
}
```

## Loading it

```python
from datasets import load_dataset

ds = load_dataset("mjbommar/opengloss-v2.2-inflections", split="train")
print(ds)
print(ds[0])
```

The shards are plain parquet, so nothing forces you through `datasets` — read them
straight, locally or over `hf://`:

```python
import polars as pl

df = pl.read_parquet("hf://datasets/mjbommar/opengloss-v2.2-inflections/data/train-*.parquet")
print(df.head())
```

```python
import duckdb

duckdb.sql("SELECT count(*) FROM 'hf://datasets/mjbommar/opengloss-v2.2-inflections/data/train-*.parquet'").show()
```

### Resolve a surface form to its lemma and part of speech

```python
import polars as pl

forms = pl.read_parquet("data/train-*.parquet")


def resolve(surface: str) -> pl.DataFrame:
    needle = surface.lower()
    return forms.filter(pl.col("form_normalized") == needle).select(
        "form", "lexeme_id", "headword", "pos", "relation"
    )


print(resolve("geese"))
print(resolve("Ran"))
```

## Identifiers, and how they compose

Every id is **derived from structure**, never randomly minted, so a consumer can
recompute one from a row and join across the whole family without a lookup table. Sense
positions are stable across regenerations: a retired sense is tombstoned, not removed, so
the indices after it never shift.

| Id | Shape | Example |
|---|---|---|
| Lexeme | `slugify(headword)` | `abseil` |
| Sense | `{lexeme_id}:{pos}:{index}` (zero-based) | `abseil:verb:0` |
| Rendition | `{owner_id}#{reading_level}/{register}` | `abseil:verb:0#grade_5/plain` |
| Entry-level owner | `{lexeme_id}:encyclopedia` / `:explanation` | `abseil:encyclopedia` |
| Edge | `{source_sense_id}-{type}->{target_lexeme_id}` | `abseil:verb:0-synonym->rappel` |
| Query | `{sense_id}#q{n}` (zero-based) | `abseil:verb:0#q3` |
| QA pair | `{sense_id}#qa{n}` (zero-based) | `abseil:verb:0#qa3` |
| Provenance record | `p{n}` within its entry (one-based) | `p12` |

An edge id keys on the *target's slug*, not on the target's sense, so resolving a target
never changes the id of the edge that found it.

## Reading levels and registers

A rendition is keyed on a `(reading_level, register)` pair. The canonical rendition of
every field is `(neutral, plain)`; everything else is a rewrite of it.

| `reading_level` | Who it is written for | Rough CCSS band |
|---|---|---|
| `neutral` | The canonical text: an adult general reader, no level targeted | — |
| `grade_1` | Beginning readers; short sentences, common words | K–1 |
| `grade_5` | Upper elementary | 4–5 |
| `grade_10` | Secondary | 9–10 |
| `college` | Undergraduate and above; technical vocabulary allowed | 11–CCR |

| `register` | What changes | Reading it |
|---|---|---|
| `plain` | Nothing — the neutral register | The default |
| `informal` | Conversational, contractions, everyday words | How you'd say it to a friend |
| `formal` | Full forms, precise hedging, no contractions | How you'd write it in a report |
| `technical` | Domain vocabulary, exact conditions | How a specialist would state it |
| `marketing` | Benefit-first, persuasive framing | A genre, not a formality level |

`marketing` sits on the register axis for convenience but is a *genre* value rather than
a point on the formality scale — worth remembering if you train a formality classifier on
this column.

## Related datasets

Everything below is built from the same store and joins on `lexeme_id` / `sense_id`.

| Dataset | Grain | What it holds |
|---|---|---|
| [`opengloss-v2.2-lexicon`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-lexicon) | one row per lexeme | One row per lexeme: kind, morphology, etymology, encyclopedia, contrasts, sense ids, provenance summary. |
| [`opengloss-v2.2-senses`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-senses) | one row per live sense | One row per live sense: canonical gloss, 8 gloss renditions, examples, resolved relations, synthetic queries, grounded QA pairs. |
| [`opengloss-v2.2-definitions`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-definitions) | one row per gloss rendition | One row per gloss rendition (canonical included): reading level, register, text, readability grade. |
| [`opengloss-v2.2-examples`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-examples) | one row per example rendition | One row per example sentence with the headword's character span, its reading level and register. |
| [`opengloss-v2.2-encyclopedia`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-encyclopedia) | one row per encyclopedia rendition · one row per lexical-explanation rendition | One row per encyclopedia article rendition, plus an `explanation` config for the "why this word" prose. |
| [`opengloss-v2.2-etymology`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-etymology) | one row per entry with an etymology | One row per entry with an etymology: prose summary, ordered language trail, cognates, references. |
| **`opengloss-v2.2-inflections`** (this one) | one row per inflected, derived or lemma form | One row per inflected or derived form, plus the lemma itself: a flat form→lemma lookup. |
| [`opengloss-v2.2-relations`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-relations) | one row per live relation edge · one row per removed relation edge | One row per semantic edge, resolved to target sense ids; a `tombstoned` config recovers the edges the reconcile pass removed. |
| [`opengloss-v2.2-queries`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-queries) | one row per synthetic query | One row per synthetic retrieval query, across eight query styles, tagged to the sense it should retrieve. |
| [`opengloss-v2.2-qa-pairs`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-qa-pairs) | one row per question/answer pair | One row per grounded question/answer pair, with the rendition ids the answer cites. |
| [`opengloss-v2.2-contrasts`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-contrasts) | one row per contrast paragraph | One row per "X vs Y" paragraph on a synonym/antonym/confusable edge, with a verdict on the edge. |
| [`opengloss-v2.2-provenance`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-provenance) | one row per provenance record | One row per recorded generation call: stage, model, tokens, cost, run id — the audit trail. |
| [`opengloss-v2.2-retrieval-pairs`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-retrieval-pairs) | one row per mined pair | Word-in-context and doc2query-shaped (text_a, text_b, label) pairs mined from the store for free. |
| [`opengloss-v2.2-retrieval-triples`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-retrieval-triples) | one row per (query, positive, negative) triple | MS MARCO-style (query, positive, negative) triples whose hard negatives come from the graph. |
| [`opengloss-v2.2-qrels`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-qrels) | one row per query, with its whole graded candidate list · one row per document in the retrieval corpus | Graded TREC relevance judgements (0–3) plus the document corpus and listwise candidate lists. |
| [`opengloss-v2.2-pretrain`](https://huggingface.co/datasets/mjbommar/opengloss-v2.2-pretrain) | one row per rendered document | Entries serialised into plain-prose dictionary, thesaurus, encyclopedia and usage-note documents. |

## Known limitations

- **It is synthetic.** Every string here was written by a language model against a
  schema, not transcribed from a corpus or checked by a lexicographer. It is
  well-formed and internally consistent; it is not attested usage, and it will contain
  confident errors. Do not use it as ground truth about what a word means.
- **Judge scores 70.2/100 (core + tier 2) and 66.7/100 (tier 3).**
  A different model family (Claude Opus) scored fixed 40-entry
  stratified samples at the close of each build. Sample statistics, not per-entry
  guarantees, and the judge is itself a model.
- **Relation precision is the weakest axis.** Relations were judged for validity and the
  ones that failed were demoted rather than asserted; symmetric reciprocity finished at
  94.2% for synonyms and 94.3% for antonyms,
  and 4,524 senses were left with no relation at all. Treat a
  single edge as a hypothesis, not a fact; treat the aggregate graph as usable.
- **`core`, `tier2`, `tier3`, `tier4` and `tier5` are deliberately partial.** 148,292 lexemes across `core`, `tier2`, `tier3`, `tier4` and `tier5` received the text stages (glosses, examples, encyclopedia) but not the queries, QA pairs, contrasts or register renditions. The coverage table above gives the exact per-field share; nothing is hidden behind an average.
- **The encyclopedia is entry-level.** One article per *headword*, about the headword as
  a whole. On a polysemous entry it is not a description of any one sense, and it is
  never used as a positive for one (D-71). It is entry-level reference prose, not a
  specialist article.

## Sources and licences

This release is **Creative Commons Attribution 4.0 International (CC-BY 4.0)**. Of 148,292 lexemes in this release, **38,100** (the tier-5 entries whose `source` column reads `wordnet-3.0`) are derived from Princeton WordNet 3.0: their glosses, examples, relations and derivationally related forms, plus WordNet's own capitalisation of the headword (D-78).

The [WordNet License](https://wordnet.princeton.edu/license-and-commercial-use) permits use, copying, modification and
distribution without fee, provided its notice is preserved:

The [WordNet License](https://wordnet.princeton.edu/license-and-commercial-use) notice is quoted in full on the `opengloss-v2.2-lexicon` and `opengloss-v2.2-senses` cards; this repo's WordNet-derived rows are governed by the same terms.

## Citation

```bibtex
@misc{bommarito2025opengloss,
  title  = {OpenGloss: A Synthetic Encyclopedic Dictionary and Semantic Knowledge Graph},
  author = {Bommarito, Michael J., II},
  year   = {2025},
  eprint = {2511.18622},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2511.18622}
}
```

Tier-5 entries additionally derive from Princeton WordNet 3.0 (D-78):

```bibtex
@article{miller1995wordnet,
  title   = {WordNet: A Lexical Database for English},
  author  = {Miller, George A.},
  journal = {Communications of the ACM},
  volume  = {38},
  number  = {11},
  pages   = {39--41},
  year    = {1995}
}

@book{fellbaum1998wordnet,
  title     = {WordNet: An Electronic Lexical Database},
  editor    = {Fellbaum, Christiane},
  publisher = {MIT Press},
  year      = {1998}
}
```

## License

Released under **Creative Commons Attribution 4.0 International (CC-BY 4.0)**. Attribution to the OpenGloss project is required;
commercial use is permitted. See [Sources and licences](#sources-and-licences) above for
the Princeton WordNet License that additionally covers this release's tier-5 entries.