Triple
T15807454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grant County International Airport |
E383254
|
entity |
| Predicate | hasVeryLongRunway |
P20697
|
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: [Grant County International Airport, hasVeryLongRunway, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVeryLongRunway Context triple: [Grant County International Airport, hasVeryLongRunway, yes]
-
A.
hasRunwayLengthCategory
chosen
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
-
B.
hasRunwayExtension
Indicates that a runway has an additional extended section beyond its original length.
-
C.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
-
D.
runwayLength
Indicates the length of a runway associated with an airport or airfield.
-
E.
hasLongestRunwayIn
Indicates that an entity possesses the runway of greatest length within a specified location or region.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b52751348190964e82463ce9dd20 |
completed | April 16, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69e0053b847c8190945726c3ddac21cc |
completed | April 15, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:48 a.m.