Triple
T36491802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | miniImageNet |
E899067
|
entity |
| Predicate | hasTypicalTrainImagesPerClass |
P48407
|
FINISHED |
| Object | 600 |
—
|
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: 600 | Statement: [miniImageNet, hasTypicalTrainImagesPerClass, 600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalTrainImagesPerClass Context triple: [miniImageNet, hasTypicalTrainImagesPerClass, 600]
-
A.
hasTrainingImages
Indicates that an entity is associated with one or more images used to train a model or learning system.
-
B.
hasApproximateNumberOfImages
Indicates that an entity is associated with a quantity of images that is approximate rather than an exact count.
-
C.
trainingDatasetSize
chosen
Indicates the number of data samples or instances used to train a model or system.
-
D.
trainsetComposition
Indicates the relationship specifying how a trainset is composed from its constituent vehicles or units.
-
E.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ffa15d53208190ab8574d6c7913e18 |
completed | May 9, 2026, 9:04 p.m. |
| PD | Predicate disambiguation | batch_69ff9eee681c81909434e79c627cb528 |
completed | May 9, 2026, 8:54 p.m. |
Created at: May 3, 2026, 4:10 p.m.