Triple

T7775110
Position Surface form Disambiguated ID Type / Status
Subject Metro de Medellín E179170 entity
Predicate hasLine P35 FINISHED
Object Line T E62461 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: Line T | Statement: [Metro de Medellín, hasLine, Line T]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line T
Context triple: [Metro de Medellín, hasLine, Line T]
  • A. T Line chosen
    The T Line is a streetcar-style light rail route within the Sound Transit Link system serving Tacoma, Washington.
  • B. Line B
    Line B is a major Mexico City Metro route that runs diagonally across the city, connecting central areas with northeastern suburbs and serving as an important commuter corridor.
  • C. Line B
    Line B is one of the main tram routes in the Reims tramway network in Reims, France, providing urban public transport across key areas of the city.
  • D. Line B
    Line B is one of the main lines of the Buenos Aires Underground, running through key commercial and residential areas of the city.
  • E. Line B
    Line B is one of the main routes of the Strasbourg tramway network, serving key districts and connecting important transit hubs across the city.
  • 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_69c69f30602c819082ab52cd4af5c592 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c7046331e0819080ec1a5c23c27cd7 completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8c7f12b888190b10479c3db81cce2 completed March 29, 2026, 6:34 a.m.
Created at: March 27, 2026, 4:11 p.m.