Triple

T17794592
Position Surface form Disambiguated ID Type / Status
Subject Peñón Viejo station E444256 entity
Predicate railwayLine P848 FINISHED
Object Line A — NE NERFINISHED

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: Line A | Statement: [Peñón Viejo station, railwayLine, Line A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line A
Context triple: [Peñón Viejo station, railwayLine, Line A]
  • A. Line A chosen
    Line A is a line of the Mexico City Metro system that serves the eastern part of the metropolitan area, connecting central Mexico City with several suburban municipalities.
  • B. Line A
    Line A is the main north–south rapid transit line of the Medellín Metro system in Colombia, serving as its busiest and most central corridor.
  • C. Line A
    Line A is the primary route of the Bilbao tram system, serving key areas of the city with modern light rail service.
  • D. Line A
    Line A is one of the main routes of the Porto Metro light rail system in Porto, Portugal, connecting key urban and suburban areas.
  • E. Line A
    Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9efe370819095cd219b143ae727 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48799f9608190bc97264c849278a0 completed April 19, 2026, 7:43 a.m.
Created at: April 10, 2026, 10:13 a.m.