Triple

T15366881
Position Surface form Disambiguated ID Type / Status
Subject MAX Light Rail E367436 entity
Predicate hasLine P35 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 Light Rail, hasLine, MAX Blue Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MAX Blue Line
Context triple: [MAX Light Rail, hasLine, 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
    The Blue Line is one of Boston's MBTA rapid transit routes, running primarily between downtown Boston and the coastal communities of East Boston and Revere.
  • D. 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.
  • E. Blue Line
    The Blue Line is a primary light rail route of the San Diego Trolley system, running through key corridors of the San Diego metropolitan area.
  • 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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4a7cdc8190b7b48c97e774c306 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff0b4e968c8190a16824ee3ede13b2 completed May 9, 2026, 10:24 a.m.
Created at: April 10, 2026, 3:18 a.m.