Triple

T15120509
Position Surface form Disambiguated ID Type / Status
Subject Sibu Division E361157 entity
Predicate containsUrbanCentre P11388 FINISHED
Object Sibu E361159 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: Sibu | Statement: [Sibu Division, containsUrbanCentre, Sibu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sibu
Context triple: [Sibu Division, containsUrbanCentre, Sibu]
  • A. Sibu chosen
    Sibu is a major town in the central region of Sarawak, Malaysia, known as a commercial and transportation hub on the island of Borneo.
  • B. Kasibu
    Kasibu is a rural municipality in the province of Nueva Vizcaya in the Philippines, known for its mountainous terrain and agricultural economy.
  • C. Sibutu
    Sibutu is a small but strategically located island in the southern Philippines near the maritime border with Malaysia, known for its role in regional sea routes and its predominantly Muslim population.
  • D. Oshikwambi
    Oshikwambi is a regional dialect of the Oshiwambo language spoken by the Kwambi people in northern Namibia.
  • E. Bongwe
    Bongwe is a dialect of the Duala language spoken by the Duala people of Cameroon.
  • 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_69d85a06450081909c5a14ea9851a15e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0059e036c8190959ff3bde8f2356f completed April 15, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a61136081908198806944c81808 completed May 9, 2026, 11:28 a.m.
Created at: April 10, 2026, 3:06 a.m.