---
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 **Superseded by [OpenGloss v2.1](https://huggingface.co/datasets/mjbommar/opengloss-v2.1-examples)** (2026-09-07): 109,633 lexemes and 250,003 live senses — twice this release's coverage — plus a new `opengloss-v2.1-inflections` form→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](#related-datasets) for the rest.

## What's new in v2.0 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. v2.0 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.
6. **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](https://huggingface.co/datasets/mjbommar/opengloss-v1.3-definitions); 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:**

```json
{
  "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

```python
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://`:

```python
import polars as pl

df = pl.read_parquet("hf://datasets/mjbommar/opengloss-v2.0-examples/data/train-*.parquet")
print(df.head())
```

```python
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

```python
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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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`](https://huggingface.co/datasets/mjbommar/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

```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}
}
```

## License

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