Triple

T15292701
Position Surface form Disambiguated ID Type / Status
Subject Gunbalanya E365565 entity
Predicate hasAirport P105 FINISHED
Object Gunbalanya Airport E1154499 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: Gunbalanya Airport | Statement: [Gunbalanya, hasAirport, Gunbalanya Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gunbalanya Airport
Context triple: [Gunbalanya, hasAirport, Gunbalanya Airport]
  • A. Gunbalanya Airport chosen
    Gunbalanya Airport is a small regional airfield serving the remote community of Gunbalanya (formerly Oenpelli) in the Northern Territory of Australia.
  • B. Butaritari Airport
    Butaritari Airport is a small public airfield serving the island of Butaritari in Kiribati, providing vital domestic air connections for the local population.
  • C. Dumna Airport
    Dumna Airport is a domestic airport serving the city of Jabalpur in the Indian state of Madhya Pradesh.
  • D. Kadala Airport
    Kadala Airport is the main commercial airport serving the city of Chita in eastern Siberia, Russia.
  • E. Sayak Airport
    Sayak Airport is the main commercial airport serving Siargao Island in the Philippines, providing air access for tourists and residents to this popular surfing destination.
  • 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_69d85a103d9081908c1ea6c4c73ac8e3 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03682ea488190ac82fdbd0e855d34 completed April 16, 2026, 1:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a6582908190a9a56652e9ccc5a1 completed May 9, 2026, 11:28 a.m.
Created at: April 10, 2026, 3:15 a.m.