mjbommar/opengloss-v2.1-lexicon

Superseded by OpenGloss v2.2 (2026-09-08): 148,292 live lexemes and 288,304 senses — tier 5 closes the WordNet gap (38,100 entries imported from Princeton WordNet 3.0 and enriched), inflected-form headwords are folded onto their lemmas, and every inherited field carries a migrate provenance record. v2.1 stays published for reproducibility.

OpenGloss v2.1 — Lexicon

The entry-level view of OpenGloss v2.1: one row per lexeme, with everything that belongs to the entry rather than to one of its meanings — the kind discriminator, per-POS morphology, structured etymology, the lexical explanation, the encyclopedia article at every reading level it was written for, and the "X vs Y" contrast paragraphs written about the entry's symmetric relation edges. Sense-level content lives in opengloss-v2.1-senses, joined on lexeme_id.

Part of the OpenGloss v2.1 release family — 16 datasets built from one store of 109,633 lexemes and 250,003 live senses, all joinable on derived ids. See Related datasets for the rest.

What's new in v2.1 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. v2.1 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 v2.0

v2.0 (2026-09-05) covered the frequency-ranked single words. v2.1 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 v2.1 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-v2.1-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 v2.1 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 v2.1 as a new release, not a delta.

Scope: fewer headwords, far more per headword

v2.1 is not a superset of v1.3. It covers 109,633 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; if you need graded renditions, resolved relations, spans, or retrieval supervision, use v2.1.

	v1.3	v2.1
Lexemes	205,988	109,633
Senses	565,604	250,003
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	109,633
Live senses	250,003
Rows in this dataset	109,633
Entries with an etymology	109,593
Encyclopedia renditions	348,899
Contrast paragraphs	83,081
Recorded generation cost	$779.21

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

Tier	Lexemes	Live senses
core	10,000	33,405
tier2	31,886	75,326
tier3	12,838	25,593
tier4	54,909	115,679

Coverage by tier

The release was built in 4 frequency-ranked passes (core, tier2, tier3 and tier4) 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
Canonical gloss	sense	100.0%	100.0%	100.0%	100.0%
Controlled domain tag	sense	100.0%	100.0%	100.0%	100.0%
Gloss at 4 reading levels	sense	100.0%	99.9%	99.9%	100.0%
Gloss in 4 registers	sense	100.0%	100.0%	0.0%	0.0%
At least one example	sense	100.0%	99.9%	99.8%	99.5%
Examples at 4 reading levels	sense	99.0%	99.6%	99.7%	99.5%
At least one relation	sense	99.4%	99.2%	99.3%	99.2%
Synthetic retrieval queries	sense	100.0%	100.0%	0.0%	0.0%
Grounded QA pairs	sense	99.8%	99.6%	0.0%	0.0%
Etymology	lexeme	100.0%	100.0%	99.8%	100.0%
Lexical explanation	lexeme	100.0%	100.0%	100.0%	100.0%
Encyclopedia (neutral)	lexeme	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%
Contrast paragraphs	lexeme	72.8%	55.1%	0.0%	0.0%

Parts of speech

POS	Live senses
noun	161,934
adjective	50,519
verb	30,593
adverb	4,763
interjection	1,048
preposition	404
determiner	337
pronoun	269
conjunction	129
numeral	7

Domain roots

Root	Live senses
people_society	36,400
everyday_life	27,193
language	21,437
science	21,228
business	18,721
nature	18,155
technology	17,984
law_government	16,738
health	13,998
arts	13,772
education	13,131
humanities	12,069
mathematics	7,577
history	7,173
sports_recreation	4,427

Files

Files	Config	Rows	Shards	Size
data/train-*.parquet	default	109,633	2	351.2 MB

Fields

109,633 rows, one row per lexeme.

Field	Type	Description
lexeme_id	string	Entry id: slugify(headword). The family's join key.
headword	string	The entry's surface headword.
language	string	BCP-47-ish language tag; en throughout v2.1.
kind	string	Lexeme kind discriminator: simplex, compound, phrasal_verb, idiom, proper_noun, abbreviation, affix, function_word.
status	string	complete, partial or retired.
tier	string	core, tier2 or tier3 (see coverage table).
pos_list	list<string>	Parts of speech this entry has, in stored order.
sense_ids	list<string>	Ids of this entry's live senses.
n_live_senses	int32	How many live (non-tombstoned) senses.
n_retired_senses	int32	How many senses are tombstoned. Retired senses are kept in the store but never exported (D-1: deletion is a tombstone, not a removal).
is_stopword	bool	Whether the entry is a closed-class function word.
frequency	double	Raw Wikipedia occurrence count, when known.
zipf	double	Zipf-scaled frequency (van Heuven), when known.
morphology	list<struct<pos, plural, past_tense, past_participle, present_participle, third_person_singular, comparative, superlative, derivations, collocations>>	One struct per POS entry: inflected forms, derivations, collocations.
etymology	struct<summary, segments, cognates, references>	Prose summary plus the ordered language trail, cognates and references (null when the entry has none).
lexical_explanation	list<struct<reading_level, register, text, readability_grade>>	"Why this word" prose, one struct per (reading level, register).
encyclopedia	list<struct<reading_level, register, text, readability_grade>>	The entry-level encyclopedia article, one struct per (reading level, register). Entry-level, never a stand-in for one sense (D-71).
contrasts	list<struct<edge_id, target_sense_id, verdict, text>>	One "X vs Y" paragraph per symmetric relation edge, with the verdict on whether the edge is what it claims.
provenance_summary	struct<models, total_cost_usd, n_records>	Which models wrote this entry, what it cost, how many recorded calls.
created_at	string	ISO-8601 UTC timestamp of first generation.
updated_at	string	ISO-8601 UTC timestamp of the last write.

One real row:

{
  "lexeme_id": "a",
  "headword": "a",
  "language": "en",
  "kind": "function_word",
  "status": "complete",
  "tier": "tier4",
  "pos_list": [
    "determiner",
    "noun"
  ],
  "sense_ids": [
    "a:determiner:0",
    "a:noun:0",
    "a:noun:1"
  ],
  "n_live_senses": 3,
  "n_retired_senses": 0,
  "is_stopword": true,
  "frequency": 59460128.0,
  "zipf": null,
  "morphology": [
    {
      "pos": "determiner",
      "plural": null,
      "past_tense": null,
      "past_participle": null,
      "present_participle": null,
      "third_person_singular": null,
      "comparative": null,
      "superlative": null,
      "derivations": [],
      "collocations": [
        "a lot",
        "a few",
        "a little",
        "… 3 more of 6"
      ]
    },
    {
      "pos": "noun",
      "plural": null,
      "past_tense": null,
      "past_participle": null,
      "present_participle": null,
      "third_person_singular": null,
      "comparative": null,
      "superlative": null,
      "derivations": [],
      "collocations": [
        "letter A",
        "capital A",
        "uppercase A",
        "… 3 more of 6"
      ]
    }
  ],
  "etymology": {
    "summary": "The English indefinite article a derives from the Old English numeral ān 'one' and its Proto-Germanic ancestor *ainaz, ultimately from the Proto-Indo-European numeral for 'one'. The path shows the numeral functioning as a determiner in Old English, then undergoing analogical reduction of the final -n to become the unin … [truncated for this card]",
    "segments": [
      {
        "language": "Proto-Germanic",
        "language_code": null,
        "form": "*ainaz",
        "meaning": "one; a single",
        "era": "Proto-Germanic period (circa 500 BCE – 300 CE)"
      },
      {
        "language": "Old English",
        "language_code": null,
        "form": "ān",
        "meaning": "one; a single",
        "era": "Old English (ca. 5th – 11th century)"
      },
      {
        "language": "Middle English",
        "language_code": null,
        "form": "a / an",
        "meaning": "indefinite article",
        "era": "Middle English (ca. 1100 – 1500)"
      },
      "… 2 more of 5"
    ],
    "cognates": [
      "Old Norse einn",
      "Icelandic einn",
      "Danish en",
      "… 6 more of 9"
    ],
    "references": [
      "https://www.etymonline.com/word/a",
      "https://www.etymonline.com/word/one",
      "https://en.wikipedia.org/wiki/Indefinite_article"
    ]
  },
  "lexical_explanation": [
    {
      "reading_level": "neutral",
      "register": "plain",
      "text": "A is the indefinite article used before a singular count noun to indicate a non-specific member of a group, as in “a book” or “a teacher.” It signals that the noun refers to one example of a class rather than a particular, previously identified item. In this function, it contrasts with the definite article “the,” which … [truncated for this card]",
      "readability_grade": null
    }
  ],
  "encyclopedia": [
    {
      "reading_level": "neutral",
      "register": "plain",
      "text": "The word a functions primarily as an indefinite article in English, signaling non-specific reference. It is also the name of the first letter of the alphabet, written as both A (uppercase) and a (lowercase). The indefinite article helps learners mark a noun as one member of a class rather than a particular instance, en … [truncated for this card]",
      "readability_grade": null
    },
    {
      "reading_level": "grade_5",
      "register": "plain",
      "text": "The word a usually works as an indefinite article, a grammar word for one non-special thing. It can introduce one thing from a group. For example, a cat means any one cat, not one special cat. The word a also names the alphabet’s first letter. We write it as A or a. Writers use a to make sentences and stories flow. Use … [truncated for this card]",
      "readability_grade": 5.13
    },
    {
      "reading_level": "college",
      "register": "plain",
      "text": "In English, a primarily functions as an indefinite article, marking a singular noun as an unspecified member of a class rather than a particular individual. The same form names the alphabet’s first letter, which appears as uppercase A and lowercase a. As an article, it introduces new or non-specific referents, supporti … [truncated for this card]",
      "readability_grade": 13.5
    }
  ],
  "contrasts": [],
  "provenance_summary": {
    "models": [
      "gpt-5.4-nano",
      "gpt-5.6-luna",
      "rule:classify_kind_deterministic",
      "… 5 more of 8"
    ],
    "total_cost_usd": 0.005152325,
    "n_records": 41
  },
  "created_at": "2025-11-28T11:11:31.098372+00:00",
  "updated_at": "2026-09-07T04:38:30.189401+00:00"
}

Loading it

from datasets import load_dataset

ds = load_dataset("mjbommar/opengloss-v2.1-lexicon", 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://:

import polars as pl

df = pl.read_parquet("hf://datasets/mjbommar/opengloss-v2.1-lexicon/data/train-*.parquet")
print(df.head())

import duckdb

duckdb.sql("SELECT count(*) FROM 'hf://datasets/mjbommar/opengloss-v2.1-lexicon/data/train-*.parquet'").show()

Entries whose encyclopedia article was written for grade 5

import polars as pl

lex = pl.read_parquet("data/train-*.parquet")
grade5 = (
    lex.explode("encyclopedia")
    .filter(pl.col("encyclopedia").struct.field("reading_level") == "grade_5")
    .select("headword", pl.col("encyclopedia").struct.field("text"))
)
print(grade5.head())

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-v2.1-lexicon (this one)	one row per lexeme	One row per lexeme: kind, morphology, etymology, encyclopedia, contrasts, sense ids, provenance summary.
opengloss-v2.1-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-v2.1-definitions	one row per gloss rendition	One row per gloss rendition (canonical included): reading level, register, text, readability grade.
opengloss-v2.1-examples	one row per example rendition	One row per example sentence with the headword's character span, its reading level and register.
opengloss-v2.1-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-v2.1-etymology	one row per entry with an etymology	One row per entry with an etymology: prose summary, ordered language trail, cognates, references.
opengloss-v2.1-inflections	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-v2.1-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-v2.1-queries	one row per synthetic query	One row per synthetic retrieval query, across eight query styles, tagged to the sense it should retrieve.
opengloss-v2.1-qa-pairs	one row per question/answer pair	One row per grounded question/answer pair, with the rendition ids the answer cites.
opengloss-v2.1-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-v2.1-provenance	one row per provenance record	One row per recorded generation call: stage, model, tokens, cost, run id — the audit trail.
opengloss-v2.1-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-v2.1-retrieval-triples	one row per (query, positive, negative) triple	MS MARCO-style (query, positive, negative) triples whose hard negatives come from the graph.
opengloss-v2.1-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-v2.1-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 94.3% for synonyms and 95.3% for antonyms, and 1,890 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 and tier4 are deliberately partial. 109,633 lexemes across core, tier2, tier3 and tier4 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.

Citation

@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}
}

License

Released under Creative Commons Attribution 4.0 International (CC-BY 4.0). Attribution to the OpenGloss project is required; commercial use is permitted.