Triple

T13294390
Position Surface form Disambiguated ID Type / Status
Subject Nawada E316641 entity
Predicate hasNearbyAirport P4363 FINISHED
Object Gaya Airport E301314 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: Gaya Airport | Statement: [Nawada, hasNearbyAirport, Gaya Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gaya Airport
Context triple: [Nawada, hasNearbyAirport, Gaya Airport]
  • A. Gaya Airport chosen
    Gaya Airport is an international airport in the Indian state of Bihar that primarily serves the city of Gaya and nearby Buddhist pilgrimage sites such as Bodh Gaya.
  • B. Tari Airport
    Tari Airport is a regional airfield serving the town of Tari and surrounding areas in Hela Province, Papua New Guinea.
  • C. Ranai Airport
    Ranai Airport is the main civil and military airport serving the remote Natuna Islands in Indonesia’s Riau Islands province.
  • D. Begumpet Airport
    Begumpet Airport is the former primary airport of Hyderabad, India, now used mainly for military, training, and charter operations after being superseded by Rajiv Gandhi International Airport.
  • E. Awang Airport
    Awang Airport is a domestic airport serving the province of Maguindanao in the southern Philippines.
  • 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_69d806b349908190a9a61dd9323bf153 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99079c8508190b6208db9affcbc0e completed April 11, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f74610b2c481909497296e999226e8 completed May 3, 2026, 12:56 p.m.
Created at: April 9, 2026, 9:28 p.m.