Triple
T10602424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soar cognitive architecture |
E275783
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | symbolic cognitive architecture |
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: symbolic cognitive architecture Context triple: [Soar cognitive architecture, instanceOf, symbolic cognitive architecture]
-
A.
cognitive science research center
A cognitive science research center is an interdisciplinary institution that investigates the nature of mind, intelligence, and cognition through collaborative studies in psychology, neuroscience, computer science, linguistics, philosophy, and related fields.
-
B.
knowledge representation framework
A knowledge representation framework is a structured system of formalisms, models, and conventions used to encode, organize, and manipulate information so that it can be interpreted and reasoned about by humans and machines.
-
C.
knowledge representation formalism
A knowledge representation formalism is a structured, often mathematically grounded scheme for encoding information about the world so that it can be interpreted and manipulated by computational systems.
-
D.
semantic framework
A semantic framework is a structured system of concepts, rules, and relationships used to define, interpret, and reason about meaning within a particular domain or language.
-
E.
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.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
Created at: April 8, 2026, 7:31 p.m.