Triple

T22240817
Position Surface form Disambiguated ID Type / Status
Subject Dalton Municipal Airport E549715 entity
Predicate hasName P744 FINISHED
Object Dalton Municipal Airport NE NERFINISHED

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: Dalton Municipal Airport | Statement: [Dalton Municipal Airport, hasName, Dalton Municipal Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dalton Municipal Airport
Context triple: [Dalton Municipal Airport, hasName, Dalton Municipal Airport]
  • A. Dalton Municipal Airport chosen
    Dalton Municipal Airport is a public general aviation airport serving the city of Dalton and the surrounding region in northwest Georgia.
  • B. Deming Municipal Airport
    Deming Municipal Airport is a public-use airport serving the city of Deming and the surrounding region in southwestern New Mexico.
  • C. Blosser Municipal Airport
    Blosser Municipal Airport is a public-use airport serving the city of Concordia in north-central Kansas.
  • D. Dillant–Hopkins Airport
    Dillant–Hopkins Airport is a public regional airport serving the city of Keene and the surrounding area in southwestern New Hampshire.
  • E. Machesney Airport
    Machesney Airport was a former airfield in Illinois that played a key role in the early aviation history of the Machesney Park area.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e4102b881909cf47d3768e25c19 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f132140ed481909ab0d4022756a4ba completed April 28, 2026, 10:17 p.m.
Created at: April 16, 2026, 8:38 p.m.