菜单
Run the full VeritasReason semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | extract |
| description | Run the full VeritasReason semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings. |
Run the full extraction pipeline. Usage: /veritasreason:extract [file_path | "inline text"]
$ARGUMENTS = file path, inline text in quotes, or blank (uses active editor file).
1. Resolve the source.
$ARGUMENTS is a readable file path → text = open(path).read()2. Clear the result cache to prevent cross-invocation pollution:
from veritasreason.semantic_extract.cache import _result_cache
_result_cache.clear()
3. Run the full pipeline:
from veritasreason.semantic_extract import (
NamedEntityRecognizer,
RelationExtractor,
EventDetector,
CoreferenceResolver,
TripletExtractor,
ExtractionValidator,
)
# Named Entity Recognition
ner = NamedEntityRecognizer()
entities = ner.extract(text)
# Relation Extraction
rel = RelationExtractor()
relations = rel.extract(text)
# Event Detection
evt = EventDetector()
events = evt.extract(text)
# Coreference Resolution — resolve pronouns/aliases before extraction
coref = CoreferenceResolver()
resolved_text = coref.resolve(text)
# Triplet Extraction (subject–predicate–object)
triplet = TripletExtractor()
triplets = triplet.extract(resolved_text)
# Validate quality
validator = ExtractionValidator()
issues = validator.validate(entities, relations)
4. Report validator warnings above results:
⚠ ExtractionValidator: <warning message>
5. Return results as Markdown tables:
Entities (N total)
| Label | Type | Confidence | Span |
|---|
Relations (M total)
| Source | Relation Type | Target | Confidence |
|---|
Events (K total)
| Label | Type | Participants | Confidence |
|---|
Triplets (J total)
| Subject | Predicate | Object | Confidence |
|---|
6. Summary line:
Extracted: N entities, M relations, K events, J triplets — from <source>
For large files (>50KB), process in chunks and show a progress indicator. Highlight any entities appearing in the context graph already (ContextGraph.has_node(label)) with [in graph] tag.
VeritasReason full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
Analyze cause-and-effect relationships in the VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on VeritasReason knowledge graphs.
Full decision lifecycle in VeritasReason � record, query, find precedents (hybrid/advanced), analyze influence, explain, insights dashboard, list, and record exceptions. Uses AgentContext, ContextGraph, DecisionQuery, CausalChainAnalyzer, DecisionRecorder.
Detect duplicate entities, duplicate groups, and relationship duplicates in VeritasReason using fuzzy matching, schema heuristics, and graph similarity.
Generate, inspect, and use node/text embeddings in VeritasReason — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link, top-links, batch, pairwise.