Triple

T6639306
Position Surface form Disambiguated ID Type / Status
Subject Line 7 (Madrid Metro) E150543 entity
Predicate hasStation P35 FINISHED
Object San Fernando E184740 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: San Fernando | Statement: [Line 7 (Madrid Metro), hasStation, San Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Fernando
Context triple: [Line 7 (Madrid Metro), hasStation, San Fernando]
  • A. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • B. San Fernando
    San Fernando is a locality within the municipality of Huixquilucan in the State of Mexico, forming part of the greater Mexico City metropolitan area.
  • C. San Fernando
    San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
  • D. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • E. San Fernando chosen
    San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, known for its naval base, salt marshes, and historical role in the Spanish War of Independence.
  • 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_69c687f0ceb08190bf40807bfc605fa5 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6aff1fe8081908c32db341b0fb354 completed March 27, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e455edb88190983f74f39e55665c completed March 27, 2026, 8:11 p.m.
Created at: March 27, 2026, 2 p.m.