Triple
T805074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Codex |
E17412
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | artificial intelligence system |
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: artificial intelligence system Context triple: [Codex, instanceOf, artificial intelligence system]
-
A.
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.
-
B.
information system
An information system is an organized combination of people, processes, data, and technology designed to collect, process, store, and distribute information to support decision-making and control in an organization.
-
C.
socio-technical system
A socio-technical system is an interconnected arrangement of people, technologies, and organizational structures whose interactions jointly shape how work is performed and outcomes are produced.
-
D.
virtual assistant
A virtual assistant is an AI-powered software agent that interacts with users through natural language to perform tasks, answer questions, and provide personalized support across digital platforms.
-
E.
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.
- 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.