Triple
T23884912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kearney Regional Airport |
E600302
|
entity |
| Predicate | offersAirlineService |
P143422
|
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, offersAirlineService, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersAirlineService Context triple: [Kearney Regional Airport, offersAirlineService, yes]
-
A.
offersFlights
Indicates that one entity provides or makes available flight services or routes to another entity or destination.
-
B.
airlineService
Indicates that an airline operates transportation services (such as flights) between specified locations or for specified routes.
-
C.
offersScheduledPassengerService
chosen
Indicates that an entity provides planned, regular passenger transportation services according to a published schedule.
-
D.
offersServiceIn
Indicates that a provider makes a particular service available within a specified location or jurisdiction.
-
E.
airlineServiceModel
Indicates a relationship where an airline operates according to, or is characterized by, a particular service model (such as full-service, low-cost, or hybrid).
- 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.