Triple

T5731281
Position Surface form Disambiguated ID Type / Status
Subject Kualanamu International Airport E126388 entity
Predicate operator P179 FINISHED
Object Angkasa Pura II E515566 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: Angkasa Pura II | Statement: [Kualanamu International Airport, operator, Angkasa Pura II]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Angkasa Pura II
Context triple: [Kualanamu International Airport, operator, Angkasa Pura II]
  • A. Angkasa Pura II chosen
    Angkasa Pura II is an Indonesian state-owned enterprise that manages and operates numerous major airports across western Indonesia.
  • B. Angkasa Pura I
    Angkasa Pura I is an Indonesian state-owned enterprise that manages and operates numerous major airports across central and eastern Indonesia.
  • C. Adisutjipto International Airport
    Adisutjipto International Airport is the main commercial airport serving the Yogyakarta region on the island of Java, Indonesia.
  • D. Husein Sastranegara International Airport
    Husein Sastranegara International Airport is the main commercial airport serving the city of Bandung in West Java, Indonesia.
  • E. Halim Perdanakusuma International Airport
    Halim Perdanakusuma International Airport is a major airport in Jakarta, Indonesia, serving both commercial flights and military operations.
  • 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_69c0083082288190b7478cead6b5430a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c025318d688190bd878c5aa1a28728 completed March 22, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07dffe45481909eb617e40c83bd14 completed March 22, 2026, 11:40 p.m.
Created at: March 22, 2026, 3:47 p.m.