Triple

T8408856
Position Surface form Disambiguated ID Type / Status
Subject Buenos Aires Underground Line E E198569 entity
Predicate servesStation P839 FINISHED
Object Varela E411289 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: Varela | Statement: [Buenos Aires Underground Line E, servesStation, Varela]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Varela
Context triple: [Buenos Aires Underground Line E, servesStation, Varela]
  • A. Varela chosen
    Varela is a Spanish surname borne by numerous notable figures in politics, the military, arts, and public life across the Spanish-speaking world.
  • B. Montalva
    Montalva is a Spanish-language surname notably associated with Chilean president Eduardo Frei Montalva.
  • C. Aravena
    Aravena is a Chilean surname most prominently associated with Alejandro Aravena, the renowned architect and Pritzker Prize laureate.
  • D. Rivas
    Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
  • E. Briceño
    Briceño is a municipality in Colombia’s Cundinamarca Department, located within the Sabana Centro Province near the Bogotá metropolitan area.
  • 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_69ca8310df9c8190b25f16161cca3e41 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb8315a8a8819097f6da11b909b527 completed March 31, 2026, 8:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce6ce5bfe481909e05c6a34e10ae16 completed April 2, 2026, 1:19 p.m.
Created at: March 30, 2026, 6:05 p.m.