Triple

T6577957
Position Surface form Disambiguated ID Type / Status
Subject Line 11 (Madrid Metro) E157216 entity
Predicate system P730 FINISHED
Object Madrid Metro 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: Madrid Metro | Statement: [Line 11 (Madrid Metro), system, Madrid Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madrid Metro
Context triple: [Line 11 (Madrid Metro), system, Madrid Metro]
  • 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. Seville Metro
    Seville Metro is a rapid transit system serving the city of Seville and its metropolitan area in southern Spain.
  • C. 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.
  • D. 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.
  • E. Barcelona Metro
    Barcelona Metro is the rapid transit rail network serving the city of Barcelona and its metropolitan area, known for its extensive coverage and integration with other public transport modes.
  • 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_69c6882b3a108190b3a9eb343ae4162c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae74fd90819091d67eec6381d5e0 completed March 27, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c364d248190a5ce03fd52de91ba completed March 28, 2026, 8:38 p.m.
Created at: March 27, 2026, 1:54 p.m.