Triple

T8181149
Position Surface form Disambiguated ID Type / Status
Subject Subte E191062 entity
Predicate hasLine P35 FINISHED
Object Line D E198633 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 D | Statement: [Subte, hasLine, Line D]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line D
Context triple: [Subte, hasLine, Line D]
  • A. Line D chosen
    Line D is one of the main rapid transit lines of the Buenos Aires Underground, running through several central and northern neighborhoods of Argentina’s capital.
  • B. Line D
    Line D is one of the main routes of the Strasbourg tramway network, connecting key districts of the city and serving as an important corridor for daily commuters and visitors.
  • C. Line B
    Line B is one of the main rapid transit lines of the Medellín Metro system, serving several neighborhoods in the Aburrá Valley metropolitan area.
  • D. 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.
  • E. 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.
  • 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_69ca82c4538081909404325aa5639483 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4c4c2e388190b86854f8b1765e61 completed March 31, 2026, 4:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbf86f8848190a196f7c8d3ad2b36 completed April 1, 2026, 6:47 a.m.
Created at: March 30, 2026, 5:40 p.m.