Triple
T805075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Codex |
E17412
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | large language model |
C4177
|
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: large language model Context triple: [Codex, instanceOf, large language model]
-
A.
deep learning model
chosen
A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
-
B.
natural language
Natural language is a human-developed system of spoken, written, or signed communication characterized by grammar, vocabulary, and usage conventions that evolve within a linguistic community.
-
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.
intelligent personal assistant
An intelligent personal assistant is a software agent that uses artificial intelligence to understand user requests, manage tasks, and provide personalized information or services through natural language interaction.
-
E.
metalanguage
A metalanguage is a language or formal system used to describe, analyze, or define another language (the object language), including its syntax, semantics, and rules.
- 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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
Created at: March 1, 2026, 7:38 p.m.