Triple
T34786922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laplace's rule of succession |
E1002836
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | classical Bayesian method |
C25327
|
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: classical Bayesian method Context triple: [Laplace's rule of succession, instanceOf, classical Bayesian method]
-
A.
statistical inference method
chosen
A statistical inference method is a systematic procedure for drawing conclusions about a population’s properties based on observed sample data, often quantifying uncertainty through probabilities or confidence measures.
-
B.
concept in Bayesian statistics
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.
-
C.
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.
-
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.
idealized prediction method
An idealized prediction method is a theoretical procedure that, given complete and accurate information about a system and its governing rules, produces perfectly accurate forecasts of future states or outcomes.
- 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_69f76db47d408190a24fc7164439ea2d |
completed | May 3, 2026, 3:45 p.m. |
Created at: May 3, 2026, 3:59 p.m.