Triple
T30165717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayes rules |
E766785
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Bayesian decision-theoretic concept |
C23158
|
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: Bayesian decision-theoretic concept Context triple: [Bayes rules, instanceOf, Bayesian decision-theoretic concept]
-
A.
concept in Bayesian statistics
chosen
A concept in Bayesian statistics is an abstract idea or construct—such as prior, likelihood, posterior, or credible interval—that helps formalize how beliefs about unknown quantities are updated with observed data using probability.
-
B.
Bayesian state estimation technique
A Bayesian state estimation technique is a probabilistic method that recursively updates the estimated state of a system by combining prior knowledge with new noisy measurements using Bayes’ theorem.
-
C.
decision theory
Decision theory is the study of how agents should and do make rational choices under conditions of uncertainty, balancing preferences, probabilities, and outcomes.
-
D.
statistical classification
Statistical classification is the process of assigning items or observations to predefined categories or classes based on their measured features using probabilistic or algorithmic decision rules.
-
E.
machine learning paradigm
A machine learning paradigm is a conceptual framework that defines how models learn from data, including the assumptions, learning objectives, and training procedures that guide the development and application of algorithms.
- 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_69f2247a968881909d79c18f2bfcb275 |
completed | April 29, 2026, 3:32 p.m. |
Created at: April 29, 2026, 7:23 p.m.