Triple
T36491785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | miniImageNet |
E899067
|
entity |
| Predicate | hasTestSplitClassCount |
P197540
|
FINISHED |
| Object | 20 |
—
|
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: 20 | Statement: [miniImageNet, hasTestSplitClassCount, 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTestSplitClassCount Context triple: [miniImageNet, hasTestSplitClassCount, 20]
-
A.
hasValidationSplitClassCount
Indicates that there is a specified number of distinct classes present in the validation split of a dataset.
-
B.
hasSplit
Indicates that an entity has undergone a division into two or more distinct parts, groups, or components.
-
C.
hasCategorySplit
Indicates that something is divided into or associated with multiple distinct categories or subcategories.
-
D.
hasTestStructure
Indicates that an entity is associated with, or contains, a specific test-related structure used for evaluation or validation.
-
E.
containsTestSet
Indicates that one entity includes or encompasses a particular test set as part of its contents or structure.
- 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_69fe991bca608190b524e419642f4243 |
completed | May 9, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69fe979fc1c4819091fc48d63ea12063 |
completed | May 9, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69fe991abc6c81908edbb98d61c9ca73 |
completed | May 9, 2026, 2:16 a.m. |
Created at: May 3, 2026, 4:10 p.m.