Triple
T34101350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soar language |
E874576
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | cognitive modeling language |
C28692
|
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: cognitive modeling language Context triple: [Soar language, instanceOf, cognitive modeling language]
-
A.
neural network modeling language
A neural network modeling language is a specialized formalism or syntax used to define, configure, and connect neural network components and architectures in a clear, structured, and often platform-agnostic way.
-
B.
symbolic cognitive architecture
chosen
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.
Knowledge representation language
A knowledge representation language is a formal system used to encode information about the world in a structured, machine-interpretable way so that computers can reason about it.
-
D.
visual modeling language
A visual modeling language is a formal system that uses graphical notations (such as diagrams, symbols, and connectors) to represent, design, and communicate the structure and behavior of complex systems.
-
E.
cognitive neuroscience model
A cognitive neuroscience model is a theoretical or computational framework that explains how brain structures and neural processes give rise to cognitive functions such as perception, memory, decision-making, and language.
- 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_69f349a735208190a1dbfb1c2a121059 |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 1:53 a.m.