Triple
T7277806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OWL 2 EL |
E163073
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | lightweight ontology language |
C2887
|
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: lightweight ontology language Context triple: [OWL 2 EL, instanceOf, lightweight ontology language]
-
A.
Web ontology language
chosen
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.
-
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.
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.
-
E.
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.
- 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_69c6885c5964819085b209701769877f |
completed | March 27, 2026, 1:38 p.m. |
Created at: March 27, 2026, 2:59 p.m.