    Finished `test` profile [unoptimized + debuginfo] target(s) in 0.48s
     Running tests/label_precision_benchmark.rs (target/debug/deps/label_precision_benchmark-c64808f7653fac86)

running 1 test
label_precision_benchmark: model=qwen2.5:14b
label_precision_benchmark: Engine::new receives Arc<Ollama> directly (NO ArcChatProvider wrapper). fn model() should return: 'qwen2.5:14b'
test label_precision_gte_0_75_on_mock_interview has been running for over 60 seconds
  extracted: 'Africa' (id='africa') label='Entity'
  extracted: 'Amazon' (id='amazon') label='Entity'
  extracted: 'Amazon Robotics' (id='amazon robotics') label='Entity'
  extracted: 'Boston' (id='boston') label='Entity'
  extracted: 'Boston Consulting Group' (id='boston consulting group') label='Entity'
  extracted: 'COVID' (id='covid') label='Entity'
  extracted: 'C-suite' (id='csuite') label='Entity'
  extracted: 'France' (id='france') label='Entity'
  extracted: 'Hi I'm Ria Thank' (id='hi im ria thank') label='Entity'
  extracted: 'Japanese' (id='japanese') label='Entity'
  extracted: 'Morocco' (id='morocco') label='Entity'
  extracted: 'Northeastern University While' (id='northeastern university while') label='Entity'
  extracted: 'South Korea' (id='south korea') label='Entity'
  extracted: 'Tableau' (id='tableau') label='Entity'
label_precision: 0.0% (0/10 ground-truth name+label pairs matched)
  14 non-canonical labels (TD-012 indicator)
  non-canonical entities: [("Africa", "Entity"), ("Amazon", "Entity"), ("Amazon Robotics", "Entity"), ("Boston", "Entity"), ("Boston Consulting Group", "Entity"), ("COVID", "Entity"), ("C-suite", "Entity"), ("France", "Entity"), ("Hi I'm Ria Thank", "Entity"), ("Japanese", "Entity"), ("Morocco", "Entity"), ("Northeastern University While", "Entity"), ("South Korea", "Entity"), ("Tableau", "Entity")]

── PHASE 1 DIAGNOSTIC: Metrics snapshot ──
  rql.entity.label_rejected_total: NO REJECTIONS RECORDED
  Other rql.* counters in snapshot:
    rql.entity_types.default_seed_applied [("group_id", "default")] = 1
    rql.db.insert_episode_with_group_ms [] = 0
    rql.ingest.chunk_count [] = 0
    rql.db.list_entities_count [] = 0
    rql.db.list_entities_ms [] = 0
    rql.extraction.structured_call_attempt [("schema", "EntityList"), ("arm", "format_schema"), ("model", "qwen2.5:14b")] = 3
    rql.extraction.structured_call_success [("schema", "EntityList"), ("arm", "format_schema")] = 3
    rql.extraction.stage_ms [("stage", "entities")] = 0
    rql.extraction.json_parse_ok [] = 9
    rql.extraction.structured_call_attempt [("schema", "RelTypeList"), ("arm", "format_schema"), ("model", "qwen2.5:14b")] = 3
    rql.extraction.structured_call_success [("schema", "RelTypeList"), ("arm", "format_schema")] = 3
    rql.extraction.stage_ms [("stage", "relations")] = 0
    rql.extraction.structured_call_attempt [("schema", "TripletList"), ("arm", "format_schema"), ("model", "qwen2.5:14b")] = 3
    rql.extraction.fallback_ladder_step [("schema", "TripletList"), ("from_arm", "format_schema"), ("to_arm", "llm_json_repair"), ("model", "qwen2.5:14b")] = 2
    rql.extraction.structured_call_attempt [("schema", "TripletList"), ("arm", "llm_json_repair"), ("model", "qwen2.5:14b")] = 2
    rql.extraction.structured_call_success [("schema", "TripletList"), ("arm", "llm_json_repair")] = 2
    rql.extraction.stage_ms [("stage", "triplets")] = 0
    rql.extraction.entity_count [] = 0
    rql.extraction.fact_count [] = 0
    rql.extraction.structured_call_success [("schema", "TripletList"), ("arm", "format_schema")] = 1
    rql.db.insert_entity_with_group_ms [] = 0
    rql.db.set_entity_embedding_ms [] = 0
    rql.db.insert_episodic_edge_ms [] = 0
    rql.search.fts_facts_hits [] = 0
    rql.search.fts_facts_ms [] = 0
    rql.db.insert_fact_ms [] = 0
    rql.extraction.structured_call_attempt [("schema", "ContradictionVerdict"), ("arm", "format_schema"), ("model", "qwen2.5:14b")] = 5
    rql.extraction.structured_call_success [("schema", "ContradictionVerdict"), ("arm", "format_schema")] = 5
    rql.db.invalidate_fact_with_reason_ms [] = 0
    rql.ingest.total_ms [] = 0
    rql.ingest.entity_count [] = 0
    rql.ingest.fact_count [] = 0
    rql.ingest.merge_count [] = 0
    rql.ingest.contradiction_count [] = 0
    rql.db.get_entity_ms [] = 0
── END DIAGNOSTIC ──


thread 'label_precision_gte_0_75_on_mock_interview' (8780459) panicked at crates/kremory/tests/label_precision_benchmark.rs:382:5:
label_precision 0.0% (0/10) is below the 65% R1 threshold.
Pre-Phase-4 (TD-012 broken state) produces ≈ 0%. Post-L1+L2+L3 target >= 65%.
14 entities had non-canonical labels.
Check: StructuredCallBuilder FormatSchema arm is active for model 'qwen2.5:14b'.
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
test label_precision_gte_0_75_on_mock_interview ... FAILED

failures:

failures:
    label_precision_gte_0_75_on_mock_interview

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 4 filtered out; finished in 367.68s

error: test failed, to rerun pass `-p kremory --test label_precision_benchmark`
