Triple

T3199810
Position Surface form Disambiguated ID Type / Status
Subject Naga City E67022 entity
Predicate hasAirport P105 FINISHED
Object Naga Airport E328645 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: Naga Airport | Statement: [Naga City, hasAirport, Naga Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Naga Airport
Context triple: [Naga City, hasAirport, Naga Airport]
  • A. Naga Airport chosen
    Naga Airport is a domestic airport serving the city of Naga and surrounding areas in the Bicol Region of the Philippines.
  • B. Gaya Airport
    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.
  • C. Palam Airport
    Palam Airport is the former name and original airfield of Delhi’s main international airport, now known as Indira Gandhi International Airport.
  • D. Sibulan Airport
    Sibulan Airport is a domestic airport serving Dumaguete and the surrounding areas in Negros Oriental, in the Central Visayas region of the Philippines.
  • E. Tambolaka Airport
    Tambolaka Airport is a regional airport serving the western part of Sumba Island in East Nusa Tenggara, Indonesia, providing domestic connections to major Indonesian cities.
  • 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_69ad8589bd988190afa7ed2bdffb7b33 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada9ad4b1c8190bc6ad0f025f238c8 completed March 8, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24bc3695c8190abd58dbc74ca2271 completed March 12, 2026, 5:14 a.m.
Created at: March 8, 2026, 3:07 p.m.