Triple

T10304665
Position Surface form Disambiguated ID Type / Status
Subject Estadio Olímpico Pascual Guerrero E241719 entity
Predicate locatedInNeighborhood P40 FINISHED
Object San Fernando E101533 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: [Estadio Olímpico Pascual Guerrero, locatedInNeighborhood, San Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Fernando
Context triple: [Estadio Olímpico Pascual Guerrero, locatedInNeighborhood, San Fernando]
  • A. 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.
  • B. San Fernando chosen
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • C. 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.
  • D. San Fernando
    San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
  • E. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • 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_69d381ac38808190a8ca7457c85b625b completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d308f034819098da69b963eb8a02 completed April 7, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71d58416081909a010e905d70e934 completed April 9, 2026, 3:30 a.m.
Created at: April 6, 2026, 11:45 a.m.