Triple
T8483175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WaveGlow |
E200567
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | flow-based generative 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: flow-based generative model Context triple: [WaveGlow, instanceOf, flow-based generative model]
-
A.
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.
-
B.
generative AI service suite
A generative AI service suite is an integrated collection of tools and APIs that create, transform, and analyze content (such as text, images, code, or audio) using advanced machine learning models to support diverse applications and workflows.
-
C.
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.
-
D.
multimodal large language model family
A multimodal large language model family is a group of related neural models that can jointly process and generate multiple data modalities—such as text, images, audio, or video—using shared architectures, training objectives, and parameterizations.
-
E.
framework in generative grammar
A framework in generative grammar is a theoretical system of principles and formal mechanisms used to model and explain the innate structure and rules underlying human language.
- 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.