Triple

T1620113
Position Surface form Disambiguated ID Type / Status
Subject Forest Hills station E35010 entity
Predicate servesLine P839 FINISHED
Object Orange Line E45338 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: Orange Line | Statement: [Forest Hills station, servesLine, Orange Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orange Line
Context triple: [Forest Hills station, servesLine, Orange Line]
  • A. Orange Line
    The Orange Line is one of the primary rapid transit routes in the Washington Metro system, running east–west through Washington, D.C. and its Virginia and Maryland suburbs.
  • B. Orange Line chosen
    The Orange Line is a rapid transit route in the Boston metropolitan area that runs north–south through downtown as part of the MBTA subway system.
  • C. Orange Line
    The Orange Line is a major corridor of the Delhi Metro system that connects central Delhi to the Indira Gandhi International Airport and surrounding areas.
  • D. Orange Line
    The Orange Line is a rapid transit route in Chicago that connects the city's Loop with Midway International Airport as part of the Chicago "L" system.
  • E. Orange Line
    The Orange Line is a light rail route in the Dallas Area Rapid Transit (DART) system serving key destinations including Dallas/Fort Worth International Airport and several northern suburbs.
  • 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_69a886023194819080a3fccd6e325d0e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909b0738c8190b073e0ccec5e217d completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3b54b888190889555d51e563742 completed March 8, 2026, 10:09 p.m.
Created at: March 4, 2026, 7:28 p.m.