Extract

/veritasreason:extract

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).

Steps

1. Resolve the source.

• If $ARGUMENTS is a readable file path → text = open(path).read()
• If it's quoted inline text → use directly
• If blank → use the active editor file

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)

Relations (M total)

Events (K total)

Triplets (J total)

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.