Triple

T4529210
Position Surface form Disambiguated ID Type / Status
Subject Panay Island E106252 entity
Predicate hasAirport P105 FINISHED
Object Roxas Airport E284123 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: Roxas Airport | Statement: [Panay Island, hasAirport, Roxas Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Roxas Airport
Context triple: [Panay Island, hasAirport, Roxas Airport]
  • A. Roxas Airport chosen
    Roxas Airport is a domestic airport serving the city of Roxas and the surrounding province of Capiz in the Philippines.
  • B. Ramon Airport
    Ramon Airport is an international airport in southern Israel serving as a major gateway to the Negev desert and the resort city of Eilat.
  • C. Senai International Airport
    Senai International Airport is a major airport in the Malaysian state of Johor that serves the city of Johor Bahru and the surrounding southern region as a key domestic and regional air travel hub.
  • D. Supadio International Airport
    Supadio International Airport is the main commercial airport serving Pontianak and the surrounding West Kalimantan region on the island of Borneo in Indonesia.
  • E. Velana International Airport
    Velana International Airport is the main international gateway to the Maldives, serving the capital region and acting as the country’s primary hub for global air travel.
  • 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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd5779593081908593537b9239e01b completed March 20, 2026, 2:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdc55254848190940c506e75d79933 completed March 20, 2026, 10:08 p.m.
Created at: March 20, 2026, 1:03 p.m.