Triple

T5897245
Position Surface form Disambiguated ID Type / Status
Subject Trinidad E131129 entity
Predicate hasCity P316 FINISHED
Object San Fernando E79508 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: [Trinidad, hasCity, San Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Fernando
Context triple: [Trinidad, hasCity, 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 chosen
    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
    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_69c00857439c819095950754176aa58a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c036f65c1c819084cb90662af6e114 completed March 22, 2026, 6:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b159cb908190b78b78d1e854212b completed March 23, 2026, 3:19 a.m.
Created at: March 22, 2026, 3:58 p.m.