Triple
T15361542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CIFAR-100 |
E367299
|
entity |
| Predicate | includedIn |
P1393
|
FINISHED |
| Object | TorchVision datasets |
E96634
|
NE 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: TorchVision datasets | Statement: [CIFAR-100, includedIn, TorchVision datasets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TorchVision datasets Context triple: [CIFAR-100, includedIn, TorchVision datasets]
-
A.
torchvision (ecosystem)
chosen
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
B.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
C.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
-
D.
torch.utils.data.Dataset
`torch.utils.data.Dataset` is a core PyTorch abstraction that defines the interface for custom data loading, enabling indexed access to samples and integration with data loaders for efficient batching and shuffling.
-
E.
CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4607408190ab281a7f7a8012d3 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b4a181c8190bffc1ac1a86e215d |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.