Triple
T4325976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | torchvision |
E96634
|
entity |
| Predicate | hasSubmodule |
P25619
|
FINISHED |
| Object | torchvision.datasets |
E96634
|
NE FINISHED |
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: torchvision.datasets | Statement: [torchvision, hasSubmodule, torchvision.datasets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: torchvision.datasets Context triple: [torchvision, hasSubmodule, 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.
ImageNet
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
-
E.
CIFAR-100
CIFAR-100 is a widely used image classification dataset consisting of 60,000 32×32 color images across 100 object categories, commonly used to benchmark machine learning models.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubmodule Context triple: [torchvision, hasSubmodule, torchvision.datasets]
-
A.
hasSubcomponent
chosen
Indicates that one entity is a constituent part or component of another, larger entity.
-
B.
hasSisterSubsystem
Indicates that one subsystem is related to another as a sister subsystem, meaning they share a common parent system or hierarchical level.
-
C.
hasSubService
Indicates that one service includes or is composed of another, more specific service as a subordinate or component part.
-
D.
hasSubConcept
Indicates that one concept is a more specific, subordinate, or narrower idea within the scope of another, more general concept.
-
E.
hasSubProcess
Indicates that one process is composed of or includes another process as a subordinate or component step.
- F. None of above.
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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3513020f481909ff2fec3934f3002 |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d09861a4819086a88bb42a8ea2e4 |
completed | March 14, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69b34f4bec888190987fc2631498b637 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:13 p.m.