Triple
T19771939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NETL (Network Representation of Knowledge) |
E474907
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | semantic network model |
C27305
|
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: semantic network model Context triple: [NETL (Network Representation of Knowledge), instanceOf, semantic network model]
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
knowledge representation formalism
chosen
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.
-
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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
Created at: April 10, 2026, 1:48 p.m.