Triple

T6549926
Position Surface form Disambiguated ID Type / Status
Subject Trang province E151103 entity
Predicate hasAirport P105 FINISHED
Object Trang Airport E605452 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: Trang Airport | Statement: [Trang province, hasAirport, Trang Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Trang Airport
Context triple: [Trang province, hasAirport, Trang Airport]
  • A. Trang Airport chosen
    Trang Airport is a domestic airport in Trang Province, southern Thailand, serving as the main air gateway to the city of Trang and nearby coastal and island destinations.
  • B. Iki Airport
    Iki Airport is a regional airport in Nagasaki Prefecture, Japan, providing air transport services to and from Iki Island.
  • C. Muanda Airport
    Muanda Airport is a public airport serving the coastal town of Muanda in the western Democratic Republic of the Congo, providing regional air connectivity for passengers and cargo.
  • D. Utrik Airport
    Utrik Airport is a small public airstrip serving the remote Utrik Atoll in the Marshall Islands, providing essential air transport for local residents and visitors.
  • E. HEF Airport
    HEF Airport is the regional public airport serving Manassas, Virginia, handling general aviation and some corporate and charter traffic for the Washington, D.C. metropolitan area.
  • 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_69c687f3fd60819083bfa583e5bcfa71 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6ae04affc8190826ee033849f2d42 completed March 27, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e4214f28819083134e9e6fe6f393 completed March 27, 2026, 8:10 p.m.
Created at: March 27, 2026, 1:51 p.m.