ajaxdavis/donto-qwen3.8-27b-predicate-extraction-data

Donto-Qwen3.8 Predicate Extraction Data V15

This repository is the complete public data and evidence companion to ajaxdavis/donto-qwen3.8-27b-predicate-extractor.

It contains the canonical V15 extraction training/validation corpus, the validator corpus, the optional D1-repeat ablation, the once-sealed 100-document graph-first gold suite, exact tool schemas, generator/evaluator source, hashes, and audit reports.

Why this dataset exists

Donto is designed for generative abundance: preserve many free, possibly contradictory claims now and defer alignment and reconciliation. That requires an extractor that does more than identify named entities or produce a short summary. The training data decomposes documents into bounded semantic tasks so a model can learn atomic coverage, attribution, negation, modality, derived views, structural relations, recovery passes, and finite stopping.

Contents

Path	Rows	Targets	Documents/families	Purpose
data/training/train.jsonl	4,255	9,013 facts	200	Canonical V15 training mixture
data/training/validation.jsonl	1,085	2,140 facts	40	Source/template-disjoint validation
data/training/train-d1-repeat.jsonl	152	855 facts	152	Optional legacy D1 mixture ablation; not used in canonical V15
data/validator/train.jsonl	950	10,070 decisions	152	Candidate fact validator supervision
data/validator/validation.jsonl	250	2,650 decisions	40	Validator validation
data/gold-100/sealed-blind.jsonl	2,718	5,338 facts	100	Frozen graph-first extraction test
data/gold-100/validator-sealed-blind.jsonl	628	6,636 decisions	100	Validator companion to Gold 100

Canonical row schema

Each JSONL row contains:

{
  "messages": [{"role": "user", "content": "..."}],
  "tools": [{"type": "function", "function": {"name": "submit_facts"}}],
  "target": {
    "facts": [{"s": "...", "p": "...", "o": "...", "c": 0.99, "h": false}],
    "more_supported_facts": false
  },
  "meta": {
    "document_id": "...",
    "source_sha256": "...",
    "split": "train",
    "task": "d0",
    "synthetic": true
  }
}

o may be a string, number, or Boolean. The h flag distinguishes direct source-licensed claims (false) from derived/interpretive/hypothetical views (true). Evidence anchors are intentionally disabled in this release.

Validator rows replace target.facts with indexed decisions such as ACCEPT, REVERSE, REWRITE, REJECT, or AMBIGUOUS.

Task mixture

The canonical training split contains:

Task	Rows
D1 unit	1,159
Local residual	893
D0	646
Structural batch	494
D2	323
Global residual	304
Metadata	152
D1 global exhaustion	152
Reviewed-real D0	132

The synthetic generator deliberately creates incidents, quotations, conditionals, argument chains, nested beliefs, procurement decisions, appointments, coalitions, attribution, disagreement, causality, negation, and modal qualification.

Provenance tiers

Synthetic tier

• 4,123 canonical training rows from 152 generated documents;
• all 1,085 canonical validation rows from 40 generated documents;
• all 2,718 Gold-100 rows from 100 generated graph-first documents.

The generator first creates a semantic graph and then realizes it as source prose. Targets are derived from the graph, not guessed from generated prose after the fact. Generator source is under source/.

Reviewed-real tier

The training mixture includes 132 rows reconstructed from exact evidence anchors over 48 reviewed research source families. These include short historical OCR fragments and technical/research notes. They are marked with meta.synthetic=false and task reviewed_real_d0.

Do not silently collapse these provenance tiers. The mixed licensing implications are documented in DATA-LICENSE.md.

Split integrity

Canonical V15 training versus validation:

• source-family overlap: 0;
• parent-template overlap: 0;
• blind/gold rows used in training: 0;
• four operator/admission source hashes hard excluded;
• all 5,340 rendered train/validation rows fit under 4,096 tokens.

Gold 100 versus canonical V15 train/validation:

• 100 unique document/source hashes;
• 73 parent templates;
• source-hash overlap: 0;
• parent-template overlap: 0;
• 45–59 unique facts per document; mean 52.88;
• generated and hash-frozen before the first model inference over it.

The suite is named sealed-blind because it was sealed during development. It was opened once for the published V15 evaluation on 2026-08-25 and is now a public test set—not a future blind holdout.

Gold-100 benchmark result

V15 produced 4,762 exact matches from 5,338 gold facts:

• exact recall: 89.2094%;
• exact precision: 89.4440%;
• bootstrap 95% recall lower bound: 87.8240%;
• mean document exact recall: 89.5048%;
• 2,718/2,718 valid tool calls;
• zero malformed or duplicate items;
• two reflexive entity errors.

The complete prediction file is published in the linked model repository. Exact matching is deliberately strict; valid free-predicate synonyms receive no credit. Synthetic templating remains a limitation.

Reproduction

The main sources are:

• source/donto-qwen38-synthetic-corpus.rs: graph-first corpus generator;
• source/donto-qwen38-training-corpus.rs: canonical mixture builder;
• source/donto-qwen38-eval-corpus.rs: forced-tool exact evaluator.

The original generator and derived-corpus manifests are in provenance/. The complete design and admission plan is in docs/.

Known limitations

• Generated prose has more regular structure than unconstrained real documents.
• The reviewed-real tier is small and not a representative population sample.
• English dominates; multilingual extraction is untested.
• Exact keys under-credit semantic synonyms but do not solve semantic validity.
• Gold targets reflect the generator's closure, not every defensible proposition.
• The dataset contains statements about fictional and historical people; a row is extraction supervision, not an assertion that its content is true.
• Historical OCR may contain errors or objectionable language inherited from a source.
• Do not use the dataset or model as an unattended authority in high-stakes settings.

Integrity hashes

• Train: 1b3630d00d11476c7f1e941a60a442a5eb5eb57dd882b7d188ef9b85d461e738
• Validation: c360e8296e5ec7973f2fab8416d0b30e2aa850fe6b2cc618bb3fa52894d818b2
• Tool schema: e02cf3c20831eed00a05598f42f324f9112f7c283b9cd0cef43af96e294c93ab
• Gold 100: 6f16c87d06c2a0b850321b8750e54a63364937cfa2823f34f698f6c00f160f1c
• Gold-100 validator: 19a293e9d3bb142cb737b9fbef311508b7867934bd2c00c80adb13ba10342a5e

Citation

@misc{donto_qwen38_data_v15_2026,
  author       = {Davis, Thomas},
  title        = {Donto-Qwen3.8 Predicate Extraction Data V15},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/ajaxdavis/donto-qwen3.8-27b-predicate-extraction-data}}
}