Triple

T12399981
Position Surface form Disambiguated ID Type / Status
Subject Juanda International Airport E296225 entity
Predicate operator P179 FINISHED
Object Angkasa Pura I E296226 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 I | Statement: [Juanda International Airport, operator, Angkasa Pura I]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Angkasa Pura I
Context triple: [Juanda International Airport, operator, Angkasa Pura I]
  • A. Angkasa Pura I chosen
    Angkasa Pura I is an Indonesian state-owned enterprise that manages and operates numerous major airports across central and eastern Indonesia.
  • B. Angkasa Pura II
    Angkasa Pura II is an Indonesian state-owned enterprise that manages and operates numerous major airports across western 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_69d6ad9f464c81909db36d7e96e34b9e completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9401cbfd481908ee6e765da3d12cb completed April 10, 2026, 6:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63482af8c8190b277b36371979f5e completed May 2, 2026, 5:29 p.m.
Created at: April 8, 2026, 9:55 p.m.