Triple

T13048776
Position Surface form Disambiguated ID Type / Status
Subject Ilocos Norte E327393 entity
Predicate hasLocalLanguage P4185 FINISHED
Object Pangasinan NE NERFINISHED

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: Pangasinan | Statement: [Ilocos Norte, hasLocalLanguage, Pangasinan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pangasinan
Context triple: [Ilocos Norte, hasLocalLanguage, Pangasinan]
  • A. Pangasinan
    Pangasinan is a populous coastal province in the Philippines known for its rich Ilocano and Pangasinense culture, agriculture, and tourism sites such as the Hundred Islands National Park.
  • B. Pangasinan chosen
    Pangasinan is an Austronesian language spoken primarily in the Pangasinan province and surrounding areas of northwestern Luzon in the Philippines.
  • C. Pampanga
    Pampanga is a province in the Central Luzon region of the Philippines, known for its rich culinary heritage, vibrant festivals, and significant role in the country’s history and culture.
  • D. Sorsogon
    Sorsogon is a province in the Bicol Region of the Philippines known for its coastal landscapes, whale shark interactions in Donsol, and rich Bikolano culture.
  • E. Zambales
    Zambales is a coastal province in the Central Luzon region of the Philippines, known for its beaches, mangoes, and ethnolinguistic diversity.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8076e64308190904fb5c93517c901 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d980b8811c81908577f092e2736610 completed April 10, 2026, 10:59 p.m.
Created at: April 9, 2026, 8:57 p.m.