Triple
T10602476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | physical symbol system hypothesis |
E275784
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | theory in 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: theory in artificial intelligence Context triple: [physical symbol system hypothesis, instanceOf, theory in 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.
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.
-
C.
knowledge representation formalism
A knowledge representation formalism is a structured, often mathematically grounded scheme for encoding information about the world so that it can be interpreted and manipulated by computational systems.
-
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.
game-playing AI
A game-playing AI is an artificial intelligence system designed to analyze game states, make strategic decisions, and execute actions to achieve optimal performance or victory within a defined set of game rules.
- F. None of above. chosen
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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
Created at: April 8, 2026, 7:31 p.m.