Triple
T6981692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanly County Airport |
E161860
|
entity |
| Predicate | serves aviation type |
P71775
|
FINISHED |
| Object | general aviation |
—
|
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: general aviation | Statement: [Stanly County Airport, serves aviation type, general aviation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serves aviation type Context triple: [Stanly County Airport, serves aviation type, general aviation]
-
A.
servesAviationType
chosen
Indicates that one entity provides services or functions specifically for a particular type or category of aviation.
-
B.
servesAirportType
Indicates that a transportation service or facility provides service to, or is designated for, a specific type or category of airport.
-
C.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
-
D.
isAviationElementFor
Indicates that something serves as an aviation-related component, feature, or element specifically associated with or used for a particular object, system, or context.
-
E.
usedByAircraftType
Indicates that something (such as equipment, infrastructure, or a procedure) is employed or operated by a specific type or category of 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_69c68855dc0481909b4c7e9e9ed273db |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db6d3f3c8190b0121f7934440c34 |
completed | March 27, 2026, 7:33 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:31 p.m.