Triple

T16898611
Position Surface form Disambiguated ID Type / Status
Subject Madrid public transport network E424375 entity
Predicate hasComponent P35 FINISHED
Object Madrid light rail E140792 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 light rail | Statement: [Madrid public transport network, hasComponent, Madrid light rail]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madrid light rail
Context triple: [Madrid public transport network, hasComponent, Madrid light rail]
  • A. Madrid Metro
    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 chosen
    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. 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.
  • E. Valencia Metro
    Valencia Metro is the rapid transit system serving the city of Valencia and its metropolitan area in Spain.
  • 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_69d889da3e8c8190a2b118f383f0beac completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3c8da7b0481909111358871875023 completed April 18, 2026, 6:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01232992ec81909dbbd2e28111e8f4 completed May 11, 2026, 12:30 a.m.
Created at: April 10, 2026, 5:29 a.m.