Triple
T8483174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WaveGlow |
E200567
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | neural network model |
C4177
|
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: neural network model Context triple: [WaveGlow, instanceOf, neural network model]
-
A.
deep learning model
chosen
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.
-
B.
neural network API
A neural network API is an interface that allows developers to build, configure, train, and deploy neural network models programmatically without managing low-level implementation details.
-
C.
image recognition model
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.
recurrent artificial neural network
A recurrent artificial neural network is a type of neural network where connections form directed cycles, allowing information to persist over time and enabling the modeling of sequential or temporal data.
-
E.
deep learning framework
A deep learning framework is a software library or platform that provides tools, abstractions, and optimized components to design, train, and deploy neural network models efficiently.
- 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:12 p.m.