Triple
T18016595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KeypointRCNN |
E431011
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | PyTorch torchvision model |
C4178
|
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 torchvision model Context triple: [KeypointRCNN, instanceOf, PyTorch torchvision model]
-
A.
PyTorch ecosystem project
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.
-
B.
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.
-
C.
image recognition model
chosen
An image recognition model is a computational system that analyzes visual input to automatically identify, classify, and sometimes localize objects, patterns, or features within images.
-
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.
deep learning model
A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
- 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.