Triple
T36491783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | miniImageNet |
E899067
|
entity |
| Predicate | hasTrainSplitClassCount |
P201397
|
FINISHED |
| Object | 64 |
—
|
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: 64 | Statement: [miniImageNet, hasTrainSplitClassCount, 64]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainSplitClassCount Context triple: [miniImageNet, hasTrainSplitClassCount, 64]
-
A.
hasValidationSplitClassCount
Indicates that there is a specified number of distinct classes present in the validation split of a dataset.
-
B.
hasTestSplitClassCount
Indicates that there is a specific number of classes present in the test split of a dataset or evaluation setup.
-
C.
isMultiClass
Indicates that an entity simultaneously belongs to or is classified under more than one class or category.
-
D.
hasThreeClasses
Indicates that an entity is associated with exactly three distinct classes.
-
E.
dataSplit
Indicates that a dataset is partitioned into distinct subsets (such as training, validation, or test sets) for separate roles in processing or evaluation.
- F. None of above. chosen
Provenance (4 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_69fff09dae088190bd8460060d778feb |
completed | May 10, 2026, 2:42 a.m. |
| PD | Predicate disambiguation | batch_69fff0027c5c8190baa5c7a15852cbe0 |
completed | May 10, 2026, 2:40 a.m. |
| PDg | Predicate description generation | batch_69fff09ce63881908e7f91a3d35d969f |
completed | May 10, 2026, 2:42 a.m. |
Created at: May 3, 2026, 4:10 p.m.