Triple
T34286908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nouvelle AI |
E879763
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | approach to artificial intelligence |
C28693
|
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: approach to artificial intelligence Context triple: [Nouvelle AI, instanceOf, approach to artificial intelligence]
-
A.
artificial intelligence
Artificial intelligence is a field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making.
-
B.
theory in artificial intelligence
chosen
A theory in artificial intelligence is a systematic, formal framework that explains, predicts, or guides the design of intelligent behavior in machines by defining underlying principles, models, and assumptions.
-
C.
test of machine intelligence
A test of machine intelligence is a systematic procedure or set of tasks designed to evaluate a machine's ability to exhibit behaviors or problem-solving capabilities that are typically associated with human cognitive processes.
-
D.
benchmark in artificial intelligence
A benchmark in artificial intelligence is a standardized task, dataset, or evaluation protocol used to quantitatively compare and assess the performance of AI models and algorithms.
-
E.
artificial intelligence framework
An artificial intelligence framework is a structured software environment that provides tools, libraries, and interfaces to design, train, deploy, and manage AI and machine learning models efficiently.
- 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_69f349b6df1c81908e5e5b6c2ab6409b |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 1:57 a.m.