Triple
T8737806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VQ-VAE |
E207427
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | discrete latent variable 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: discrete latent variable model Context triple: [VQ-VAE, instanceOf, discrete latent variable 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.
unsupervised learning method
An unsupervised learning method is a type of machine learning approach that discovers patterns, structures, or groupings in unlabeled data without predefined output targets.
-
C.
statistical distribution
A statistical distribution is a conceptual model that describes how the values of a random variable are spread or likely to occur across its possible range.
-
D.
data model
A data model is an abstract, structured representation of data and its relationships, designed to organize, define, and constrain how information is stored, accessed, and manipulated within a system.
-
E.
statistical framework
A statistical framework is a structured set of principles, assumptions, and methods that guides how data are collected, modeled, analyzed, and interpreted to draw valid inferences about underlying phenomena.
- 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
Created at: March 30, 2026, 6:38 p.m.