Triple

T9745232
Position Surface form Disambiguated ID Type / Status
Subject Bukidnon E236288 entity
Predicate hasLocalLanguage P4185 FINISHED
Object Binukid E236289 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: Binukid | Statement: [Bukidnon, hasLocalLanguage, Binukid]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Binukid
Context triple: [Bukidnon, hasLocalLanguage, Binukid]
  • A. Binukid chosen
    Binukid is an Austronesian language of the Manobo people in the Philippines, primarily used in the Bukidnon highlands of Mindanao.
  • B. Himamaylan
    Himamaylan is a coastal component city in the southern part of Negros Occidental in the Philippines, known historically as one of the province’s older settlements.
  • C. Binalong
    Binalong is a small rural village in New South Wales, Australia, known for its historic buildings and pastoral surroundings.
  • D. Kabugao
    Kabugao is a dialect of the Isnag language spoken by indigenous communities in the northern Philippines.
  • E. Guinsiliban
    Guinsiliban is a coastal municipality on the island-province of Camiguin in the Philippines, known for its rural communities and proximity to volcanic landscapes and marine attractions.
  • 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_69ca84d3e24481908a476e2231123cf9 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f2f8e648190ad94c940f9dc1de0 completed April 1, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c40b0db081908b956484e3ff44c0 completed April 5, 2026, 2:08 a.m.
Created at: March 30, 2026, 8:23 p.m.