Triple
T25366720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nancy-Essey Airport |
E632819
|
entity |
| Predicate | hasGeneralAviation |
P24735
|
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: [Nancy-Essey Airport, hasGeneralAviation, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGeneralAviation Context triple: [Nancy-Essey Airport, hasGeneralAviation, yes]
-
A.
hasGeneralAviationActivity
Indicates that an entity is involved in or supports non-commercial, private, or recreational aviation operations.
-
B.
hasGeneralAviationFacilities
chosen
Indicates that a location or airport provides facilities and services specifically for general aviation operations.
-
C.
hasCorporateAviation
Indicates that an entity operates, owns, or utilizes aircraft or aviation services for corporate or business purposes.
-
D.
typeOfAviation
Indicates the specific category or kind of aviation to which an entity belongs (e.g., commercial, military, private).
-
E.
servesAviationType
Indicates that one entity provides services or functions specifically for a particular type or category of aviation.
- 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_69e75a90c0dc819092f928b6ea0ecc72 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 21, 2026, 1:37 p.m.