Triple

T10729982
Position Surface form Disambiguated ID Type / Status
Subject Bayamón station E253045 entity
Predicate terminusFor P388 FINISHED
Object Tren Urbano E51431 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: Tren Urbano | Statement: [Bayamón station, terminusFor, Tren Urbano]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tren Urbano
Context triple: [Bayamón station, terminusFor, Tren Urbano]
  • A. Tren Urbano chosen
    Tren Urbano is a rapid transit rail system serving the San Juan metropolitan area in Puerto Rico, providing urban mass transportation across key municipalities.
  • B. Mi Tren
    Mi Tren is the electric light rail system serving the Guadalajara metropolitan area in the Mexican state of Jalisco.
  • 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. Buenos Aires Underground
    Buenos Aires Underground is the rapid transit system serving Argentina’s capital, known as the oldest subway network in Latin America and a key component of the city’s public transportation.
  • E. Metropolitano
    Metropolitano is a former operator of the San Martín Line, a railway service in Argentina.
  • 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_69d6aa5d8be481909a43218b2bfdbe95 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d70fca498881909b40163a138cfb98 completed April 9, 2026, 2:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69dff777c63c8190a989d33e8460bc2f completed April 15, 2026, 8:39 p.m.
Created at: April 8, 2026, 9:14 p.m.