Triple

T4108033
Position Surface form Disambiguated ID Type / Status
Subject Nueva Ecija E88501 entity
Predicate languageSpoken P151 FINISHED
Object Pangasinan E104727 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: Pangasinan | Statement: [Nueva Ecija, languageSpoken, Pangasinan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pangasinan
Context triple: [Nueva Ecija, languageSpoken, 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 (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_69aed9484fb881909146f4c772ad277c completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af019e23c481909578eba1c9270282 completed March 9, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69bde03b1f508190b9d5026103d3ee79 completed March 21, 2026, 12:03 a.m.
Created at: March 9, 2026, 3:40 p.m.