Triple
T18016321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MobileNetV2 |
E431005
|
entity |
| Predicate | availableInLibrary |
P57368
|
FINISHED |
| Object | PyTorch torchvision |
—
|
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: PyTorch torchvision | Statement: [MobileNetV2, availableInLibrary, PyTorch torchvision]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyTorch torchvision Context triple: [MobileNetV2, availableInLibrary, PyTorch torchvision]
-
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.
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.
-
C.
ChainerCV
ChainerCV is a computer vision toolkit built on top of the Chainer deep learning framework, providing ready-to-use models and utilities for tasks like object detection and semantic segmentation.
-
D.
Caffe Model Zoo
Caffe Model Zoo is a public collection of pre-trained deep learning models shared by the Caffe community for tasks like image classification, detection, and segmentation.
-
E.
TensorFlow Metal
TensorFlow Metal is an integration that enables TensorFlow to run efficiently on Apple GPUs via the Metal framework, accelerating machine learning workloads on macOS and iOS devices.
- 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: availableInLibrary Context triple: [MobileNetV2, availableInLibrary, PyTorch torchvision]
-
A.
usedInLibrary
chosen
Indicates that something is utilized or applied within the context of a library, such as a software library or a physical library system.
-
B.
servedByLibrary
Indicates that an entity receives services, resources, or support from a particular library.
-
C.
availableWith
Indicates that one entity can be obtained, accessed, or used in conjunction with another entity.
-
D.
availableAs
Indicates that one entity can be used, accessed, or offered in the form, role, or capacity of another entity.
-
E.
isLendingLibrary
Indicates that an entity functions as a library that lends items (such as books or media) to users.
- F. None of above.
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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
| PD | Predicate disambiguation | batch_69e3f904b8048190add43883cd7cb191 |
completed | April 18, 2026, 9:35 p.m. |
Created at: April 10, 2026, 10:24 a.m.