Triple

T7812564
Position Surface form Disambiguated ID Type / Status
Subject Dole E180721 entity
Predicate hasAirport P105 FINISHED
Object Dole–Jura Airport E666512 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: Dole–Jura Airport | Statement: [Dole, hasAirport, Dole–Jura Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dole–Jura Airport
Context triple: [Dole, hasAirport, Dole–Jura Airport]
  • A. Dole–Jura Airport chosen
    Dole–Jura Airport is a regional airport in eastern France serving the city of Dole and the surrounding Jura area with commercial and general aviation flights.
  • B. Mitiga International Airport
    Mitiga International Airport is a major airport serving Tripoli, Libya, handling both domestic and international flights.
  • C. 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.
  • D. Hector International Airport
    Hector International Airport is the primary commercial airport serving Fargo and the surrounding region in eastern North Dakota.
  • 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_69ca827f6f148190beca4e245b993506 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69caf78e198c81909d4fd227f6b71082 completed March 30, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb5a5048a88190874d7ff205151d8a completed March 31, 2026, 5:23 a.m.
Created at: March 30, 2026, 4:38 p.m.