Triple
T23330508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limbic system |
E591428
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Neural network of structures |
C46884
|
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 of structures Context triple: [Limbic system, instanceOf, Neural network of structures]
-
A.
neural network design method
A neural network design method is a systematic approach for selecting, structuring, and configuring neural network architectures and training procedures to solve specific computational or learning tasks.
-
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.
neural network component
chosen
A neural network component is a modular unit—such as a layer, activation function, or connection pattern—that processes and transforms input data as part of a larger neural architecture to enable learning and inference.
-
D.
neural networks conference
A neural networks conference is a professional gathering where researchers, practitioners, and industry experts present, discuss, and collaborate on the latest advances, applications, and theories in neural network and deep learning technologies.
-
E.
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.
- 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
Created at: April 17, 2026, 5:15 p.m.