Triple

T3597662
Position Surface form Disambiguated ID Type / Status
Subject CTA Pink Line E76177 entity
Predicate connectsWith P37 FINISHED
Object Blue Line E16457 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: [CTA Pink Line, connectsWith, Blue Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Blue Line
Context triple: [CTA Pink Line, connectsWith, Blue Line]
  • A. Blue Line chosen
    The Blue Line is a major rapid transit route in Chicago that runs between O'Hare International Airport and the city's western suburbs via downtown.
  • B. 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.
  • 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
    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.
  • E. Blue Line
    The Blue Line is a light rail route in the Dallas Area Rapid Transit (DART) system serving key neighborhoods and suburbs in the Dallas–Fort Worth metroplex.
  • 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_69ad85d8042081908af94a04c410dec0 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc19c71608190a87c98214321214c completed March 8, 2026, 6:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b52821cf088190811a201c31eadcdd completed March 14, 2026, 9:19 a.m.
Created at: March 8, 2026, 3:22 p.m.