Triple
T4415193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KMVY |
E94951
|
entity |
| Predicate | hasGeneralAviationTraffic |
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: [KMVY, hasGeneralAviationTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGeneralAviationTraffic Context triple: [KMVY, hasGeneralAviationTraffic, 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.
hasCargoTrafficType
Indicates that an entity is associated with a specific type or category of cargo traffic it handles or supports.
-
D.
hasNearbyGeneralAviationAirport
Indicates that an entity is located close to a general aviation airport, such that the airport can reasonably serve it for non-commercial or private air traffic.
-
E.
airTraffic
Indicates the movement and flow of aircraft through airspace, including their routes, density, and interactions while in flight.
- 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_69b34539638c8190abfea3eb29425210 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b354eabb2481908ad10d21e1379e7f |
completed | March 13, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:29 p.m.