Triple

T8868988
Position Surface form Disambiguated ID Type / Status
Subject Morgan Boulevard station E211097 entity
Predicate servedByLine P1293 FINISHED
Object Blue Line E26310 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: Blue Line | Statement: [Morgan Boulevard station, servedByLine, Blue Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Blue Line
Context triple: [Morgan Boulevard station, servedByLine, Blue Line]
  • A. Blue Line chosen
    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.
  • B. 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.
  • C. Blue Line
    The Blue Line is one of the primary routes of the MetroLink light rail system serving the St. Louis metropolitan area.
  • D. Blue Line
    The Blue Line is one of the main rapid transit corridors of the Hyderabad Metro system in Hyderabad, India.
  • E. Blue Line
    The Blue Line is one of the aerial cable car routes in La Paz–El Alto’s Mi Teleférico urban transit system, providing high-altitude public transportation across the Bolivian cities.
  • 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_69ca838d3c7c8190a849566d5afd2b11 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc61257a548190955ad71f4c8704d5 completed April 1, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc1bee344819084ec6e20ea01728f completed April 3, 2026, 1:33 p.m.
Created at: March 30, 2026, 6:51 p.m.