Triple
T33095337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flagon |
E846892
|
entity |
| Predicate | subjectAircraftCategory |
P91661
|
FINISHED |
| Object | military aircraft |
—
|
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: military aircraft | Statement: [Flagon, subjectAircraftCategory, military aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectAircraftCategory Context triple: [Flagon, subjectAircraftCategory, military aircraft]
-
A.
targetAircraftCategory
Indicates the category or type of aircraft that is the intended target of an action or operation.
-
B.
typicalAircraftTypeCategory
Indicates the general class or category of aircraft type that is most commonly associated with or used in a given context.
-
C.
planeCategory
Indicates the classification or type of a plane within a defined categorization scheme.
-
D.
typeOfAviation
chosen
Indicates the specific category or kind of aviation to which an entity belongs (e.g., commercial, military, private).
-
E.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
- 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_69f3495590dc8190aa04f3dec74ce976 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fd2cf39b0c8190811b8a6fa9410560 |
completed | May 8, 2026, 12:23 a.m. |
| PD | Predicate disambiguation | batch_69fd2ad8dd988190a9899701ba00d917 |
completed | May 8, 2026, 12:14 a.m. |
Created at: May 1, 2026, 1:26 a.m.