Triple
T26940034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | No. 1666 Heavy Conversion Unit RAF |
E678486
|
entity |
| Predicate | aircraftCategoryTrained |
P34146
|
FINISHED |
| Object | heavy bombers |
—
|
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: heavy bombers | Statement: [No. 1666 Heavy Conversion Unit RAF, aircraftCategoryTrained, heavy bombers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftCategoryTrained Context triple: [No. 1666 Heavy Conversion Unit RAF, aircraftCategoryTrained, heavy bombers]
-
A.
aircraftTrainedOn
Indicates that an aircraft has been used as the platform or subject for training a person or crew in its operation or related skills.
-
B.
hasFixedWingTrainingRole
chosen
Indicates that an entity serves in a training capacity specifically related to the operation or use of fixed-wing aircraft.
-
C.
targetAircraftCategory
Indicates the category or type of aircraft that is the intended target of an action or operation.
-
D.
usedTrainerAircraft
Indicates that an entity employed a particular trainer aircraft for training or instructional purposes.
-
E.
hasFixedWingTraining
Indicates that an entity has received training in operating or working with fixed-wing aircraft.
- 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_69eeeb4d69588190a7c912164a1c37b3 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 6:18 a.m.