Triple
T15466583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drift technology |
E372044
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | neural-link system |
C12903
|
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-link system Context triple: [Drift technology, instanceOf, neural-link system]
-
A.
fictional neural interface system
chosen
A fictional neural interface system is an imagined technology that directly links the human brain with computers or networks to enable seamless communication, control, and data exchange through thought alone.
-
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.
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.
-
D.
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.
-
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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
Created at: April 10, 2026, 3:33 a.m.