Triple
T32008102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Wells Airport |
E817325
|
entity |
| Predicate | hasParkingForAircraft |
P67493
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Norman Wells Airport, hasParkingForAircraft, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkingForAircraft Context triple: [Norman Wells Airport, hasParkingForAircraft, true]
-
A.
hasParkingApron
Indicates that a location or facility includes a designated parking apron area for vehicles or aircraft.
-
B.
hasParkingFor
chosen
Indicates that a place or facility provides designated parking spaces suitable for a specified type of vehicle or user.
-
C.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
D.
hasHeliport
Indicates that an entity possesses or is equipped with a heliport facility for helicopter landing and takeoff.
-
E.
hasLandingFacilities
Indicates that a location or structure is equipped with facilities that allow aircraft or other vehicles to land there.
- 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_69f348f9e5d081908cc3f57c4942af52 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fedec693b08190b0f8bfdb921e0766 |
completed | May 9, 2026, 7:14 a.m. |
| PD | Predicate disambiguation | batch_69fede16c1d48190a20d8a9c5722c307 |
completed | May 9, 2026, 7:11 a.m. |
Created at: May 1, 2026, 12:15 a.m.