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KaifAhmad1andClaude Sonnet 4.6
b9af181625
- Add `extract_temporal_bounds: bool = False` to `extract_relations_llm()`.
When True the LLM prompt is extended with a calibrated confidence scale
and four few-shot examples; each returned Relation gains valid_from,
valid_until, temporal_confidence, and temporal_source_text in metadata.
Low confidence (<0.5) with non-null dates logs a WARNING. Default False
preserves 100% backward compatibility.
- Add `RelationWithTemporalOut` / `RelationsWithTemporalResponse` Pydantic
schemas so the four temporal fields are captured from structured LLM
output (separate from RelationOut which uses extra="ignore").
- New `semantica/kg/temporal_normalizer.py` — `TemporalNormalizer` class
(zero LLM calls, pure regex + dateutil arithmetic):
* normalize(value) → (start, end) UTC datetimes or None
* Resolution order: ISO 8601 → partial dates (year/month/Q) →
ambiguity detection → domain phrase map → relative phrases
* normalize_phrase(phrase) → metadata dict or None
* Default phrase map covers 13 domains: General, Policy, Healthcare,
Drug Discovery, Cybersecurity, Supply Chain, Finance, Energy
* TemporalAmbiguityWarning for DD/MM/YYYY-style ambiguous inputs
* Custom phrase_map at construction (merged over defaults)
- Add `TemporalAmbiguityWarning(UserWarning)` to exceptions.py.
- Export `TemporalNormalizer` from `semantica/kg/__init__.py`.
- Propagate `extract_temporal_bounds` through `_extract_relations_chunked`
and add flag to cache key to prevent cross-mode cache pollution.
- 53 new tests in tests/semantic_extract/test_temporal_extraction.py;
zero real LLM calls, suite runs in ~3.5s. 873 existing tests unaffected.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>