Triple
T18016636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | torch.utils.data.Dataset |
E431012
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | PyTorch class |
C15629
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: PyTorch class Context triple: [torch.utils.data.Dataset, instanceOf, PyTorch class]
-
A.
torch
A torch is a portable light source, traditionally a stick with a combustible material at one end and in modern usage often a handheld electric device, used to illuminate dark areas.
-
B.
PyTorch ecosystem project
chosen
A PyTorch ecosystem project is a library, tool, or framework that extends or integrates with PyTorch to support tasks such as model development, training, deployment, or domain-specific applications.
-
C.
machine learning model class
A machine learning model class is a blueprint that defines the structure, parameters, and learning behavior of models that can be instantiated to learn patterns from data and make predictions or decisions.
-
D.
PyTorch accelerator backend
A PyTorch accelerator backend is a hardware-specific execution layer that optimizes and dispatches tensor operations to devices like GPUs, TPUs, or specialized accelerators to improve training and inference performance.
-
E.
scikit-learn class
A scikit-learn class is a Python object that encapsulates a specific machine learning component (such as an estimator, transformer, or model selection tool) with a consistent API for fitting to data and making predictions or transformations.
- F. None of above.
Provenance (1 batch)
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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
Created at: April 10, 2026, 10:24 a.m.