Triple

T13183366
Position Surface form Disambiguated ID Type / Status
Subject Luzon Central Plain E313783 entity
Predicate hasCity P316 FINISHED
Object Cabanatuan E473591 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: Cabanatuan | Statement: [Luzon Central Plain, hasCity, Cabanatuan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cabanatuan
Context triple: [Luzon Central Plain, hasCity, Cabanatuan]
  • A. Cabanatuan City chosen
    Cabanatuan City is a highly urbanized commercial and transportation hub in the Philippine province of Nueva Ecija, historically known as the "Tricycle Capital of the Philippines."
  • B. Dipaculao
    Dipaculao is a coastal municipality in the Philippine province of Aurora known for its beaches, surfing spots, and scenic mountain landscapes.
  • C. Tayug
    Tayug is a landlocked agricultural municipality in the province of Pangasinan in the Philippines, known for its rice farming and rural community life.
  • D. Tarlac
    Tarlac is a landlocked province in the Central Luzon region of the Philippines known for its culturally diverse population and agricultural economy.
  • E. Cauayan
    Cauayan is a rapidly developing component city located in the province of Isabela in the Cagayan Valley region of the Philippines.
  • 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_69d806ae1e08819090d95bfe1538cc17 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c4a0b0081908027bf77442ff5ff completed April 10, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf0513f88190b2405ffc32f1e9c7 completed May 10, 2026, 12:19 a.m.
Created at: April 9, 2026, 9:15 p.m.