Triple

T1025053
Position Surface form Disambiguated ID Type / Status
Subject MAX Green Line E22120 entity
Predicate connectsWith P37 FINISHED
Object MAX Blue Line E21652 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: MAX Blue Line | Statement: [MAX Green Line, connectsWith, MAX Blue Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MAX Blue Line
Context triple: [MAX Green Line, connectsWith, MAX Blue Line]
  • A. MAX Blue Line chosen
    MAX Blue Line is a light rail service in the Portland, Oregon metropolitan area that connects downtown Portland with eastern and western suburbs as part of the region’s MAX Light Rail system.
  • B. Blue Line
    The Blue Line is one of the main lines of the Lisbon Metro system, serving key central and northern areas of Portugal’s capital city.
  • C. Blue Line
    Blue Line is a light rail line in the Los Angeles Metro Rail system that connects downtown Los Angeles with Long Beach and was the system’s inaugural route.
  • D. Blue Line
    The Blue Line is one of the color-coded rapid transit routes in the Washington Metro system, running through key parts of Washington, D.C. and its Virginia suburbs.
  • E. Blue Line
    The Blue Line is one of the primary heavy-rail transit routes in Atlanta’s MARTA system, running east–west across the metropolitan area and serving key urban and suburban stations.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a493d6e380819097b384986ffc315c completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7f4c66c8190b6098fb72c1465a3 completed March 1, 2026, 10:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad1c8c828481909013039009446dc0 completed March 8, 2026, 6:51 a.m.
Created at: March 1, 2026, 7:41 p.m.