Rename _collect_{facet,entity,action}_{terms,properties} to drop the
leading underscore in propindex.py and semrefindex.py.

Change list to Sequence in add_terms_batch and add_properties_batch
interfaces and implementations to satisfy covariance. Add missing
add_terms_batch to FakeTermIndex in conftest.py.

lookup_term_filtered called get_item() per scored ref — one SELECT and
full deserialization per match. The filter only needs knowledge_type
(a plain column) and range (json.loads of range_json), never the
expensive knowledge_json deserialization (64% of per-row cost).

Add get_metadata_multiple to ISemanticRefCollection that fetches only
semref_id, range_json, knowledge_type in a single batch query. Replace
the N+1 loop in lookup_term_filtered with one get_metadata_multiple call.

Benchmark (200 matches, 200 rounds): 4.38ms → 1.32ms (3.3x speedup).

Apply the same get_metadata_multiple pattern from lookup_term_filtered
to four more sites that called get_item() in a loop:

- propindex.lookup_property_in_property_index: filter by .range
- SemanticRefAccumulator.group_matches_by_type: group by .knowledge_type
- SemanticRefAccumulator.get_matches_in_scope: filter by .range
- answers.get_scored_semantic_refs_from_ordinals_iter: two-phase
  metadata filter then batch get_multiple for matching full objects

All sites now use a single batch query instead of N individual SELECTs,
skipping knowledge_json deserialization where only range or
knowledge_type is needed.

…arisons

- Use bisect_right with key=start in TextRangeCollection.contains_range
  to skip O(n) linear scan (O(log n) for non-overlapping point ranges)
- Replace TextLocation allocations in TextRange __eq__/__lt__/__contains__
  with a shared _effective_end returning tuples
- Skip pydantic validation in get_metadata_multiple by constructing
  TextLocation/TextRange directly from JSON