Triple
T30446402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AIXI |
E774590
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | theoretical model of 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: theoretical model of artificial intelligence Context triple: [AIXI, instanceOf, theoretical model of artificial intelligence]
-
A.
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.
-
B.
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.
-
C.
theoretical model
A theoretical model is an abstract, simplified representation of a system or phenomenon used to explain, predict, or understand its behavior based on underlying principles and assumptions.
-
D.
foundational artificial intelligence text
A foundational artificial intelligence text is a comprehensive, authoritative work that establishes core theories, methods, and principles of AI, serving as a primary reference for learning and advancing the field.
-
E.
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.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
Created at: April 29, 2026, 8:08 p.m.