Triple
T10023437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian networks |
E200666
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | knowledge representation formalism |
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: knowledge representation formalism Context triple: [Bayesian networks, instanceOf, knowledge representation formalism]
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
formal logic
Formal logic is the systematic study of valid reasoning and inference using precisely defined symbols, rules, and structures independent of specific content.
-
E.
Web ontology language
A web ontology language is a formal language designed for representing rich, machine-interpretable knowledge about concepts, relationships, and constraints on the web to enable automated reasoning and interoperability.
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
Created at: March 30, 2026, 8:53 p.m.