Triple
T32991865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Revolutionary Army Air Force |
E844106
|
entity |
| Predicate | usedTrainingFrom |
P36609
|
FINISHED |
| Object | Italian military advisers |
—
|
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: Italian military advisers | Statement: [National Revolutionary Army Air Force, usedTrainingFrom, Italian military advisers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedTrainingFrom Context triple: [National Revolutionary Army Air Force, usedTrainingFrom, Italian military advisers]
-
A.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
B.
usesTrainingStrategy
Indicates that one entity applies or follows a particular training strategy in carrying out its learning or optimization process.
-
C.
hasTrained
chosen
Indicates that one entity has provided training or instruction to another entity.
-
D.
usedDataFrom
Indicates that one entity utilized or relied on data originating from another entity.
-
E.
receivedTrainingIn
Indicates that one entity has undergone or been provided with training or instruction in a particular field, skill, or subject associated with another entity.
- 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_69f3494d99988190b502c68926af2c4d |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f74062b9388190b30546cf700a825c |
completed | May 3, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69f73c802b848190b61a416b7488bd96 |
completed | May 3, 2026, 12:16 p.m. |
Created at: May 1, 2026, 1:22 a.m.