Triple
T848961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-2 |
E18339
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | neural network |
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 Context triple: [GPT-2, instanceOf, neural network]
-
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.
network architecture
A network architecture is the structured design and organization of hardware, software, protocols, and communication paths that define how data flows and services are delivered within a computer network.
-
C.
neuromorphic computing initiative
A neuromorphic computing initiative is a coordinated effort to research, develop, and deploy hardware and software systems that emulate the structure and function of biological neural networks to achieve more efficient, brain-like computation.
-
D.
tensor
A tensor is a multidimensional array of numerical values that generalizes scalars, vectors, and matrices to represent data or linear relationships across multiple dimensions.
-
E.
machine learning book
A machine learning book is a structured, written resource that explains the theories, algorithms, and practical applications of machine learning to help readers understand and apply data-driven modeling techniques.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
Created at: March 1, 2026, 7:38 p.m.