Triple

T2103900
Position Surface form Disambiguated ID Type / Status
Subject Washington Metro Green Line E37149 entity
Predicate connectsWith P37 FINISHED
Object Orange Line E12021 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: [Washington Metro Green Line, connectsWith, Orange Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orange Line
Context triple: [Washington Metro Green Line, connectsWith, Orange Line]
  • A. Orange Line chosen
    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
    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.
  • C. Orange Line
    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.
  • D. 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.
  • 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_69a8861828948190924aa30c08806b3a completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abbabf7cdc81909636dff34badc1c5 completed March 7, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae3069f78c819092a6d9d903e4df13 completed March 9, 2026, 2:28 a.m.
Created at: March 4, 2026, 7:43 p.m.