Triple
T2373557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hopfield network |
E46142
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | associative memory model |
C11478
|
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: associative memory model Context triple: [Hopfield network, instanceOf, associative memory model]
-
A.
former model
A former model is an individual who previously worked professionally in modeling but has since left the industry or no longer does it as their primary occupation.
-
B.
flash memory
Flash memory is a non-volatile, solid-state storage technology that retains data without power and allows blocks of data to be electrically erased and reprogrammed.
-
C.
NAND flash memory
NAND flash memory is a type of non-volatile storage technology that stores data in arrays of memory cells using floating-gate transistors, optimized for high-density, low-cost, and fast read/write operations commonly used in SSDs, USB drives, and memory cards.
-
D.
deep learning model
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.
-
E.
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.
- F. None of above. chosen
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_69a88a145268819083e2736cb835c696 |
completed | March 4, 2026, 7:37 p.m. |
Created at: March 4, 2026, 7:56 p.m.