Triple
T23884924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kearney Regional Airport |
E600302
|
entity |
| Predicate | supportsGeneralAviationTraining |
P24735
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Kearney Regional Airport, supportsGeneralAviationTraining, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsGeneralAviationTraining Context triple: [Kearney Regional Airport, supportsGeneralAviationTraining, yes]
-
A.
hasGeneralAviationActivity
Indicates that an entity is involved in or supports non-commercial, private, or recreational aviation operations.
-
B.
hasGeneralAviationFacilities
chosen
Indicates that a location or airport provides facilities and services specifically for general aviation operations.
-
C.
hasFlightTrainingActivity
Indicates that an entity is involved in or associated with a flight training activity.
-
D.
hasFixedWingTrainingRole
Indicates that an entity serves in a training capacity specifically related to the operation or use of fixed-wing aircraft.
-
E.
hasFixedWingTraining
Indicates that an entity has received training in operating or working with fixed-wing aircraft.
- F. None of above.
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_69e295318e148190b9979d8fc02e168f |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1ccfd99d481908aae44b387853c7d |
completed | April 29, 2026, 9:18 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:24 p.m.