Triple

T15120576
Position Surface form Disambiguated ID Type / Status
Subject Sibu E361159 entity
Predicate hasAirport P105 FINISHED
Object Sibu Airport E1001325 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 Airport | Statement: [Sibu, hasAirport, Sibu Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sibu Airport
Context triple: [Sibu, hasAirport, Sibu Airport]
  • A. Sibu Airport chosen
    Sibu Airport is a regional airport serving the town of Sibu in Sarawak, Malaysia, handling domestic flights and acting as an important air transport hub for central Sarawak.
  • B. Kimbe Airport
    Kimbe Airport is a regional airport serving the town of Kimbe and the surrounding West New Britain Province in Papua New Guinea.
  • C. Vunisea Airport
    Vunisea Airport is a small public airport serving the island of Kadavu in Fiji, providing domestic connections primarily to and from the capital, Suva.
  • D. Umbu Mehang Kunda Airport
    Umbu Mehang Kunda Airport is a regional airport serving the island of Sumba in East Nusa Tenggara, Indonesia, providing domestic connections to major Indonesian cities.
  • E. Mpanda Airport
    Mpanda Airport is a regional airport in western Tanzania that serves the town of Mpanda and the surrounding Katavi Region.
  • 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_69ff0b340900819093aa210b702f124b completed May 9, 2026, 10:23 a.m.
Created at: April 10, 2026, 3:06 a.m.