Triple
T9244213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | No. 2 Operational Conversion Unit RAAF |
E222145
|
entity |
| Predicate | aircraftTypeTrainedOn |
P1524
|
FINISHED |
| Object | fighter 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: fighter aircraft | Statement: [No. 2 Operational Conversion Unit RAAF, aircraftTypeTrainedOn, fighter aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftTypeTrainedOn Context triple: [No. 2 Operational Conversion Unit RAAF, aircraftTypeTrainedOn, fighter aircraft]
-
A.
hasFixedWingTrainingRole
Indicates that an entity serves in a training capacity specifically related to the operation or use of fixed-wing aircraft.
-
B.
aircraftFlown
Indicates that an entity (typically a person or organization) operates or pilots a particular aircraft.
-
C.
aircraftType
chosen
Indicates the specific model or category of aircraft associated with an entity or event.
-
D.
hasFixedWingTraining
Indicates that an entity has received training in operating or working with fixed-wing aircraft.
-
E.
hasHelicopterTrainingRole
Indicates that an entity holds a role or position specifically related to the training or instruction of helicopter operations.
- 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_69ca83ee26cc81909ac624e190597d6d |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cd03edd37481908ea2f6dac354f04f |
completed | April 1, 2026, 11:39 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4765648190aa9445c4a22dc471 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:30 p.m.