Triple
T22202176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | parallel distributed processing |
E548708
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | connectionist approach |
C39344
|
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: connectionist approach Context triple: [parallel distributed processing, instanceOf, connectionist approach]
-
A.
associative memory model
An associative memory model is a computational or theoretical framework that stores and retrieves information based on learned relationships or patterns between items, enabling recall of one item when presented with another related cue.
-
B.
symbolic cognitive architecture
A symbolic cognitive architecture is a computational framework that models human-like cognition using explicit, manipulable symbols and rule-based processes to represent and transform knowledge.
-
C.
machine learning paradigm
chosen
A machine learning paradigm is a conceptual framework that defines how models learn from data, including the assumptions, learning objectives, and training procedures that guide the development and application of algorithms.
-
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 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.
- 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_69e11e3ecc7c8190b5f94cd8f42e9d37 |
completed | April 16, 2026, 5:37 p.m. |
Created at: April 16, 2026, 8:36 p.m.