Triple
T2726902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tallahassee International Airport |
E60214
|
entity |
| Predicate | runway18_36Length |
P6291
|
FINISHED |
| Object | 2134 meters |
—
|
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: 2134 meters | Statement: [Tallahassee International Airport, runway18_36Length, 2134 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runway18_36Length Context triple: [Tallahassee International Airport, runway18_36Length, 2134 meters]
-
A.
runwayWidth
Indicates the measured width of a runway as a spatial dimension.
-
B.
runwayLength
chosen
Indicates the length of a runway associated with an airport or airfield.
-
C.
hasRunwayLengthCategory
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
-
D.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
E.
runwaySurface
Indicates the type or condition of the surface material that a runway is made of or covered with.
- 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_69ab4b75cd908190b691ef0d1801acda |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdace9f308190964a064859612cd6 |
completed | March 7, 2026, 7:59 a.m. |
| PD | Predicate disambiguation | batch_69abd82586f88190a98f60d3247fe2d3 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.