metadata
license: cc-by-4.0
language:
- en
size_categories:
- 1M<n<10M
task_categories:
- text-classification
- token-classification
- feature-extraction
tags:
- opengloss
- synthetic
- lexicography
- english
- word-sense-disambiguation
- examples
- wic
- spans
configs:
- config_name: default
data_files:
- split: train
path: data/train-*.parquet
Superseded by OpenGloss v2.1 (2026-09-07): 109,633 lexemes and 250,003 live senses — twice this release's coverage — plus a new
opengloss-v2.1-inflectionsform→lemma lookup. v2.0 stays published for reproducibility.
OpenGloss v2.0 — Examples
Every example sentence in OpenGloss v2.0, one row at a time, each tagged to the sense it illustrates and carrying the [span_start, span_end) character offsets of the headword occurrence inside it. That combination — a sentence, the sense it uses, and where the word is — is what a word-in-context or sense-disambiguation task needs and is normally paid for by annotation. source distinguishes the per-sense examples stage's verified sentences from reading-level and register rewrites of an existing example.
Part of the OpenGloss v2.0 release family — 15 datasets built from one store of 54,724 lexemes and 137,314 live senses, all joinable on derived ids. See Related datasets for the rest.
What's new in v2.0 vs v1.3
- Schema v3. Every lexeme carries a
kinddiscriminator (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. - 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.
- 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 institutionpoints at a meaning rather than at a string. - 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.
- Derivable identifiers everywhere. v1.3 published a positional id for lexemes and
senses (
3d_model_noun_0) and nothing below that. v2.0 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. - Per-field provenance. Which model wrote a field, how many tokens it took, what it cost — published as its own dataset.
Scope: fewer headwords, far more per headword
v2.0 is not a superset of v1.3. It covers 54,724 lexemes — a frequency-ranked subset of v1.3's 205,983 — 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 v2.0.
| v1.3 | v2.0 | |
|---|---|---|
| Lexemes | 205,983 | 54,724 |
| Senses | 565,604 | 137,314 |
| 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 | 54,724 |
| Live senses | 137,314 |
| Rows in this dataset | 1,398,297 |
| Sentences carrying a headword span | 1,396,100 (99.8%) |
| From the per-sense examples stage | 490,770 |
| From rendition rewrites | 907,527 |
By tier
| Tier | Lexemes | Live senses |
|---|---|---|
core |
10,000 | 34,015 |
tier2 |
31,886 | 76,855 |
tier3 |
12,838 | 26,444 |
Coverage by tier
The release was built in three frequency-ranked passes 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 |
|---|---|---|---|---|
| Canonical gloss | sense | 100.0% | 100.0% | 100.0% |
| Controlled domain tag | sense | 100.0% | 100.0% | 100.0% |
| Gloss at 4 reading levels | sense | 100.0% | 99.9% | 99.9% |
| Gloss in 4 registers | sense | 100.0% | 100.0% | 0.0% |
| At least one example | sense | 100.0% | 99.9% | 99.8% |
| Examples at 4 reading levels | sense | 99.0% | 99.6% | 99.8% |
| At least one relation | sense | 96.8% | 97.3% | 98.0% |
| Synthetic retrieval queries | sense | 100.0% | 100.0% | 0.0% |
| Grounded QA pairs | sense | 99.8% | 99.6% | 0.0% |
| Etymology | lexeme | 100.0% | 100.0% | 99.8% |
| Lexical explanation | lexeme | 100.0% | 100.0% | 100.0% |
| Encyclopedia (neutral) | lexeme | 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% |
| Contrast paragraphs | lexeme | 72.8% | 55.1% | 0.0% |
Files
| Files | Config | Rows | Shards | Size |
|---|---|---|---|---|
data/train-*.parquet |
default | 1,398,297 | 3 | 49.7 MB |
Fields
1,398,297 rows, one row per example rendition.
| Field | Type | Description |
|---|---|---|
sense_id |
string |
Sense id: {lexeme_id}:{pos}:{index}. Join key. |
lexeme_id |
string |
Owning entry id: slugify(headword). Join key. |
headword |
string |
The owning entry's surface headword. |
pos |
string |
Part of speech of the owning POS entry (noun, verb, …). |
sense_index |
int32 |
Zero-based position of the sense within its POS entry. |
domain |
string |
Controlled domain leaf, root.leaf (nullable). |
tier |
string |
core, tier2 or tier3. |
reading_level |
string |
The rendition's reading level. |
register |
string |
The rendition's register. |
text |
string |
The example sentence. |
span_start |
int32 |
Character offset where the headword occurrence starts (null when the span could not be placed). |
span_end |
int32 |
Character offset one past the occurrence's end. |
readability_grade |
double |
Measured Flesch-Kincaid grade, when recorded. |
source |
string |
per_sense for a sentence written by the per-sense examples stage, renditions for a reading-level/register rewrite of an existing one. |
One real row:
{
"sense_id": "aaa:noun:0",
"lexeme_id": "aaa",
"headword": "aaa",
"pos": "noun",
"sense_index": 0,
"domain": "everyday_life.transportation",
"tier": "core",
"reading_level": "neutral",
"register": "plain",
"text": "I called AAA when my car wouldn’t start in the grocery store parking lot.",
"span_start": 9,
"span_end": 12,
"readability_grade": 4.2,
"source": "renditions"
}
Loading it
from datasets import load_dataset
ds = load_dataset("mjbommar/opengloss-v2.0-examples", 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-v2.0-examples/data/train-*.parquet")
print(df.head())
import duckdb
duckdb.sql("SELECT count(*) FROM 'hf://datasets/mjbommar/opengloss-v2.0-examples/data/train-*.parquet'").show()
Word-in-context items: the sentence, the sense, the span
from datasets import load_dataset
ex = load_dataset("mjbommar/opengloss-v2.0-examples", split="train")
row = ex[0]
text, start, end = row["text"], row["span_start"], row["span_end"]
print(text[:start] + "[" + text[start:end] + "]" + text[end:])
print("sense:", row["sense_id"], "|", row["reading_level"], "/", row["register"])
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-v2.0-lexicon |
one row per lexeme | One row per lexeme: kind, morphology, etymology, encyclopedia, contrasts, sense ids, provenance summary. |
opengloss-v2.0-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-v2.0-definitions |
one row per gloss rendition | One row per gloss rendition (canonical included): reading level, register, text, readability grade. |
opengloss-v2.0-examples (this one) |
one row per example rendition | One row per example sentence with the headword's character span, its reading level and register. |
opengloss-v2.0-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-v2.0-etymology |
one row per entry with an etymology | One row per entry with an etymology: prose summary, ordered language trail, cognates, references. |
opengloss-v2.0-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-v2.0-queries |
one row per synthetic query | One row per synthetic retrieval query, across eight query styles, tagged to the sense it should retrieve. |
opengloss-v2.0-qa-pairs |
one row per question/answer pair | One row per grounded question/answer pair, with the rendition ids the answer cites. |
opengloss-v2.0-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-v2.0-provenance |
one row per provenance record | One row per recorded generation call: stage, model, tokens, cost, run id — the audit trail. |
opengloss-v2.0-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-v2.0-retrieval-triples |
one row per (query, positive, negative) triple | MS MARCO-style (query, positive, negative) triples whose hard negatives come from the graph. |
opengloss-v2.0-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-v2.0-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 98.0% for synonyms and 99.1% for antonyms, and 3,709 senses were left with no relation at all. Treat a single edge as a hypothesis, not a fact; treat the aggregate graph as usable.
- Tier 3 is deliberately partial. 12,838 lexemes 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.