Triple

T8473411
Position Surface form Disambiguated ID Type / Status
Subject Buenos Aires bus network E200332 entity
Predicate integratedWith P2830 FINISHED
Object Premetro E716615 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: Premetro | Statement: [Buenos Aires bus network, integratedWith, Premetro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Premetro
Context triple: [Buenos Aires bus network, integratedWith, Premetro]
  • A. Premetro chosen
    Premetro is a light rail or tramway feeder line that connects outlying neighborhoods to the main Buenos Aires Subte (subway) network.
  • B. Metropolitano
    Metropolitano is a former operator of the San Martín Line, a railway service in Argentina.
  • C. Metros
    Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
  • D. Länsimetro
    Länsimetro is a major westward extension of the Helsinki Metro that connects central Helsinki with the neighboring city of Espoo through a series of new underground stations.
  • E. MG Metro
    The MG Metro is a performance-oriented small hatchback produced by MG in the 1980s as a sportier, tuned version of the Austin/Rover Metro.
  • 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_69ca831a4f348190bfdd09250e86ae35 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe4f4fbf481909e4fd7c078b27477 completed March 31, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce3a057b6c8190ad592110ca393ee2 completed April 2, 2026, 9:42 a.m.
Created at: March 30, 2026, 6:11 p.m.