Triple

T15756095
Position Surface form Disambiguated ID Type / Status
Subject Lago E381970 entity
Predicate operatedBy P86 FINISHED
Object Metro de Madrid E26338 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: Metro de Madrid | Statement: [Lago, operatedBy, Metro de Madrid]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metro de Madrid
Context triple: [Lago, operatedBy, Metro de Madrid]
  • A. Madrid Metro chosen
    Madrid Metro is the extensive rapid transit system serving Spain’s capital, known for its large network, frequent service, and role as a primary mode of urban transportation.
  • B. Metro Ligero de Madrid
    Metro Ligero de Madrid is a light rail system serving several suburban and peripheral areas of Madrid, complementing the city's main metro network.
  • C. Seville Metro
    Seville Metro is a rapid transit system serving the city of Seville and its metropolitan area in southern Spain.
  • D. Cercanías Madrid commuter rail
    Cercanías Madrid commuter rail is a regional train network that connects Madrid with its surrounding metropolitan and suburban areas, providing frequent and rapid transit for daily commuters.
  • E. Madrid Metro Line 1
    Madrid Metro Line 1 is one of the oldest and busiest lines of the Madrid Metro, running in a north–south direction and connecting key central stations 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e050b1ff4881909d5240d1d30f5c8b completed April 16, 2026, 3 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffb59779788190a393237f5293fe8d completed May 9, 2026, 10:30 p.m.
Created at: April 10, 2026, 4:47 a.m.