Triple
T18016646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | torch.utils.data.Dataset |
E431012
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | torch.utils.data.DataLoader |
—
|
NE NERFINISHED |
How this triple was built (3 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: torch.utils.data.DataLoader | Statement: [torch.utils.data.Dataset, usedWith, torch.utils.data.DataLoader]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: torch.utils.data.DataLoader Context triple: [torch.utils.data.Dataset, usedWith, torch.utils.data.DataLoader]
-
A.
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.
-
B.
tf.data API
The tf.data API is a TensorFlow library for building efficient, scalable input pipelines that load, preprocess, and feed data into machine learning models.
-
C.
TensorFlow Datasets
TensorFlow Datasets is a collection of ready-to-use, standardized datasets for machine learning and deep learning workflows in TensorFlow and other frameworks.
-
D.
torchvision (ecosystem)
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
E.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: torch.utils.data.DataLoader Target entity description: torch.utils.data.DataLoader is a PyTorch utility class that efficiently loads data from a dataset in mini-batches, with support for shuffling, parallel loading, and other data pipeline features.
-
A.
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.
-
B.
tf.data API
The tf.data API is a TensorFlow library for building efficient, scalable input pipelines that load, preprocess, and feed data into machine learning models.
-
C.
TensorFlow Datasets
TensorFlow Datasets is a collection of ready-to-use, standardized datasets for machine learning and deep learning workflows in TensorFlow and other frameworks.
-
D.
torchvision (ecosystem)
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
E.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
- F. None of above. chosen
Provenance (2 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9be5d0c819097e006f32d98753a |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 10:24 a.m.