Triple

T2886293
Position Surface form Disambiguated ID Type / Status
Subject Pangasinan language E59513 entity
Predicate region P40 FINISHED
Object Luzon E17124 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: Luzon | Statement: [Pangasinan language, region, Luzon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luzon
Context triple: [Pangasinan language, region, Luzon]
  • A. Luzon chosen
    Luzon is the largest and most populous island in the Philippines, home to the nation’s capital, Manila, and its main political and economic centers.
  • B. Visayas
    Visayas is a central group of islands in the Philippines known for its rich cultural heritage, vibrant festivals, and popular beach and diving destinations.
  • C. Mindanao
    Mindanao is the second-largest and southernmost major island of the Philippines, known for its diverse cultures, rich natural resources, and significant agricultural and economic role in the country.
  • D. Luzon Central Plain
    The Luzon Central Plain is a vast, fertile lowland region in central Luzon, Philippines, known as a major agricultural and population center of the country.
  • E. Pomorye
    Pomorye is a historical coastal region of northern Russia along the White Sea, traditionally inhabited by the Pomors and known for maritime trade and exploration.
  • 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_69ab4ac739188190a112f42a5a69c951 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abe0463ccc8190bf08330f40d0cfdc completed March 7, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b1de74f20881909e69b7fba1c4abaa completed March 11, 2026, 9:28 p.m.
Created at: March 6, 2026, 10:03 p.m.