Triple

T11058693
Position Surface form Disambiguated ID Type / Status
Subject LB-SL E261447 entity
Predicate appliesTo P1129 FINISHED
Object San Lázaro station E154956 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: San Lázaro station | Statement: [LB-SL, appliesTo, San Lázaro station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Lázaro station
Context triple: [LB-SL, appliesTo, San Lázaro station]
  • A. Olivos station
    Olivos station is a railway stop in the Buenos Aires metropolitan area that serves passengers on Argentina’s Mitre Line.
  • B. Balderas station
    Balderas station is a Mexico City Metro station in the city center known for its high passenger traffic and proximity to important cultural and historical landmarks.
  • C. San Lázaro metro station chosen
    San Lázaro metro station is a Mexico City Metro station that serves the area around the San Lázaro Legislative Palace, an important governmental and legislative hub.
  • D. San Pedrito station
    San Pedrito station is the western terminal of Line A on the Buenos Aires Underground (Subte) system in Argentina.
  • E. Pantitlán station
    Pantitlán station is one of Mexico City Metro’s largest and busiest transfer hubs, serving as a major eastern gateway that connects multiple metro lines and extensive surface transport.
  • 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_69d6aa98650481908609c7c56bfa7902 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d798a4f3f88190a29710f64cef9d25 completed April 9, 2026, 12:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3e75b90ec8190b1a799e0183c6784 completed April 18, 2026, 8:19 p.m.
Created at: April 8, 2026, 9:26 p.m.