Triple

T14818036
Position Surface form Disambiguated ID Type / Status
Subject Central Philippine E348369 entity
Predicate hasMember P10 FINISHED
Object Surigaonon E243832 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: Surigaonon | Statement: [Central Philippine, hasMember, Surigaonon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Surigaonon
Context triple: [Central Philippine, hasMember, Surigaonon]
  • A. Surigaonon chosen
    Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
  • B. Nasugbu
    Nasugbu is a coastal municipality in the province of Batangas, Philippines, known for its beaches, resorts, and agricultural areas.
  • C. 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.
  • D. Canlaon
    Canlaon is a city in the Philippines known for its proximity to Mount Kanlaon, an active volcano and prominent natural landmark on Negros Island.
  • E. Balamban
    Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
  • 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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decfe2c1ec81908b3dff7a5d0e85d0 completed April 14, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe64f8b4148190bc24f9a307178419 completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:50 a.m.