Triple
T26986589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | orthogonality thesis |
E679751
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | proposition in philosophy of artificial intelligence |
C13892
|
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: proposition in philosophy of artificial intelligence Context triple: [orthogonality thesis, instanceOf, proposition in philosophy of artificial intelligence]
-
A.
theory in artificial intelligence
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.
philosophical proposition
chosen
A philosophical proposition is a declarative statement that expresses a claim about reality, knowledge, value, or meaning, which can be analyzed, debated, and evaluated for its truth, coherence, or implications.
-
C.
philosophical theory of conditionals
A philosophical theory of conditionals is a systematic account of the meaning, truth-conditions, and logical behavior of “if–then” statements, explaining how they relate to reasoning, probability, and counterfactual situations.
-
D.
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.
-
E.
treatise on logic
A treatise on logic is a systematic, often formal written work that analyzes the principles of valid reasoning, argument structure, and inference.
- 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_69eeeb5138ac8190b3c273ddc659a54f |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 27, 2026, 6:49 a.m.