Triple
T30446497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Solomonoff induction |
E774592
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | formal theory of universal prediction |
C57011
|
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: formal theory of universal prediction Context triple: [Solomonoff induction, instanceOf, formal theory of universal prediction]
-
A.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal assumptions that precisely define and constrain measures of information, uncertainty, and related concepts so that theorems and results can be derived consistently.
-
B.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal principles that precisely define and constrain measures of information, uncertainty, and related concepts so that consistent theorems and results can be derived.
-
C.
pioneer of algorithmic information theory
A pioneer of algorithmic information theory is a foundational thinker who developed the core concepts and formal frameworks for measuring information, complexity, and randomness using algorithms and computation.
-
D.
Monte Carlo reinforcement learning algorithm
A Monte Carlo reinforcement learning algorithm is a method that learns optimal policies by estimating value functions from complete, sampled episodes of experience without requiring a model of the environment’s dynamics.
-
E.
object in optimal stopping theory
An object in optimal stopping theory is an abstract entity (such as a stochastic process, payoff function, or stopping rule) whose evolution or evaluation over time determines when it is best to stop observing and take an action to maximize expected reward or minimize expected cost.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
Created at: April 29, 2026, 8:08 p.m.