Triple

T2946249
Position Surface form Disambiguated ID Type / Status
Subject San Fernando E79508 entity
Predicate governingBody P46 FINISHED
Object San Fernando City Corporation E104726 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 City Corporation | Statement: [San Fernando, governingBody, San Fernando City Corporation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Fernando City Corporation
Context triple: [San Fernando, governingBody, San Fernando City Corporation]
  • A. San Fernando, La Union
    San Fernando, La Union is a coastal city in northern Luzon, Philippines, known as the administrative, commercial, and educational hub of the Ilocos Region.
  • B. San Fernando chosen
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • C. 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.
  • D. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • E. 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.
  • 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_69ad8b1089588190b74d9e2505e45762 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad98b3f86c819094526c2af611bfb5 completed March 8, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69b08692105c81908a3a146376b7417f completed March 10, 2026, 9:01 p.m.
Created at: March 8, 2026, 2:56 p.m.