Triple

T22933334
Position Surface form Disambiguated ID Type / Status
Subject Buton Regency E569505 entity
Predicate formerCapital P3417 FINISHED
Object Baubau NE NERFINISHED

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: Baubau | Statement: [Buton Regency, formerCapital, Baubau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Baubau
Context triple: [Buton Regency, formerCapital, Baubau]
  • A. Baubau chosen
    Baubau is a coastal city in Southeast Sulawesi, Indonesia, known as a cultural and historical center of the Wolio-speaking Butonese people.
  • B. Tarakan
    Tarakan is an island off the northeastern coast of Borneo in Indonesia, historically significant for its oil resources and as a strategic battleground during World War II.
  • C. Makasar
    Makasar is a district in East Jakarta, Indonesia, known as a primarily residential and urban area within the capital’s eastern region.
  • D. Luwuk
    Luwuk is a coastal town and regional economic center located on the eastern coast of Central Sulawesi, Indonesia.
  • E. Ternate
    Ternate is a coastal municipality in the province of Cavite in the Philippines, known for its beaches and historical significance.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2458f7d008190901dccbaebeaba24 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f181337ff881909d90cf3f5bae7516 completed April 29, 2026, 3:55 a.m.
Created at: April 17, 2026, 3:44 p.m.