extract

/semantica:extract

Run the full extraction pipeline. Usage: /semantica:extract [file_path | "inline text"]

$ARGUMENTS = file path, inline text in quotes, or blank (uses active editor file).

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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 semantica.semantic_extract.cache import _result_cache
_result_cache.clear()

3. Run the full pipeline:

from semantica.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>