Triple

T5933254
Position Surface form Disambiguated ID Type / Status
Subject Bencoolen E131985 entity
Predicate modernLocation P8489 FINISHED
Object Bengkulu, Indonesia E98565 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: Bengkulu, Indonesia | Statement: [Bencoolen, modernLocation, Bengkulu, Indonesia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bengkulu, Indonesia
Context triple: [Bencoolen, modernLocation, Bengkulu, Indonesia]
  • A. Bengkulu chosen
    Bengkulu is a province on the southwest coast of the Indonesian island of Sumatra, known for its Indian Ocean shoreline and colonial history.
  • 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. Malaka Regency
    Malaka Regency is an administrative region in the western part of Timor Island in Indonesia, known for its predominantly rural communities and agricultural-based economy.
  • D. Jambi
    Jambi is a province on the eastern coast of central Sumatra in Indonesia, known for its oil production, plantations, and the Batanghari River.
  • E. Keningau
    Keningau is a major inland town and administrative district in the interior region of the Malaysian state of Sabah.
  • 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_69c0085c55dc8190aa90e242c956e2fa completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0389f6fc881909527b928838ffcdd completed March 22, 2026, 6:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0c064d2a4819096085668182cfde1 completed March 23, 2026, 4:24 a.m.
Created at: March 22, 2026, 4 p.m.