Triple

T14090171
Position Surface form Disambiguated ID Type / Status
Subject Cotabato E339106 entity
Predicate hasCity P316 FINISHED
Object Kidapawan E344343 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: Kidapawan | Statement: [Cotabato, hasCity, Kidapawan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kidapawan
Context triple: [Cotabato, hasCity, Kidapawan]
  • A. Kidapawan chosen
    Kidapawan is a city in the Philippines that serves as the capital of Cotabato province on the island of Mindanao.
  • B. Kabankalan
    Kabankalan is a major inland city in the province of Negros Occidental in the Philippines, known as a commercial and agricultural hub in the southern part of the island.
  • C. Dipaculao
    Dipaculao is a coastal municipality in the Philippine province of Aurora known for its beaches, surfing spots, and scenic mountain landscapes.
  • D. Bansalan
    Bansalan is a municipality in the province of Davao del Sur in the Philippines, known for its agricultural economy and rural communities.
  • E. Nabunturan
    Nabunturan is a landlocked municipality in the Philippines known as the administrative and commercial center of the province of Davao de Oro on Mindanao island.
  • 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_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5ee3213c8190af2853a2a5b302a2 completed April 14, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8a9eeff48190b8aa9a601574395a completed May 8, 2026, 7:02 a.m.
Created at: April 9, 2026, 10:21 p.m.