Triple

T15789035
Position Surface form Disambiguated ID Type / Status
Subject Estadio Metropolitano E382813 entity
Predicate operator P179 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: [Estadio Metropolitano, operator, Metro de Madrid]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metro de Madrid
Context triple: [Estadio Metropolitano, operator, 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_69d86da16e188190b89af699f1ed0bfe completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e054048ff48190ad107c890ef73166 completed April 16, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbbd605c8190ae2b1289ae9570c3 completed May 10, 2026, 1:13 a.m.
Created at: April 10, 2026, 4:48 a.m.