Triple
T33220300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Range Regional Airport |
E850400
|
entity |
| Predicate | servesSecondaryUse |
P154671
|
FINISHED |
| Object | general aviation |
—
|
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: general aviation | Statement: [Range Regional Airport, servesSecondaryUse, general aviation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesSecondaryUse Context triple: [Range Regional Airport, servesSecondaryUse, general aviation]
-
A.
hasSecondaryUsage
Indicates that an entity is associated with an additional, non-primary function or purpose beyond its main intended use.
-
B.
secondaryAreaOfUse
Indicates that an entity has an additional, non-primary context, domain, or purpose in which it is used.
-
C.
servesUse
Indicates that one entity is used by or functions to serve the purpose or needs of another entity.
-
D.
hasServingUse
Indicates that something is used or intended to be used for serving (e.g., food, drink, or portions).
-
E.
secondaryBuildingUse
chosen
Indicates how a building is used in a secondary or supplementary capacity in addition to its primary function.
- 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_69f3496083dc8190b229bb6932dc548b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff59b33a38819086cc9aa19b81748b |
completed | May 9, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69ff587758f88190a39c2164341dc554 |
completed | May 9, 2026, 3:53 p.m. |
Created at: May 1, 2026, 1:30 a.m.