Triple
T15756476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hodgkin–Huxley model |
E381979
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | computational neuroscience model |
C7185
|
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: computational neuroscience model Context triple: [Hodgkin–Huxley model, instanceOf, computational neuroscience model]
-
A.
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.
-
B.
theoretical neuroscientist
A theoretical neuroscientist develops mathematical, computational, and conceptual models to explain how neural systems give rise to cognition, behavior, and brain dynamics.
-
C.
model of computation
chosen
A model of computation is an abstract mathematical framework that defines how algorithms are represented and executed, specifying the rules, operations, and resources available for performing computations.
-
D.
neuroscience research program
A neuroscience research program is an organized, long-term scientific initiative that systematically investigates the structure, function, and development of the nervous system to advance understanding of brain and behavior.
-
E.
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.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
Created at: April 10, 2026, 4:47 a.m.