Triple
T35689926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian nonparametrics |
E1031259
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | branch of Bayesian statistics |
C60842
|
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: branch of Bayesian statistics Context triple: [Bayesian nonparametrics, instanceOf, branch of Bayesian statistics]
-
A.
branch of statistics
chosen
A branch of statistics is a specialized area within the broader field of statistics that focuses on particular types of data, methods, or applications, such as inferential statistics, descriptive statistics, or Bayesian statistics.
-
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.
branch of mathematics
A branch of mathematics is a major subdivision of the mathematical sciences that focuses on a specific set of concepts, structures, and methods, such as algebra, geometry, or analysis.
-
E.
statistical framework
A statistical framework is a structured set of principles, assumptions, and methods that guides how data are collected, modeled, analyzed, and interpreted to draw valid inferences about underlying phenomena.
- 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_69f76e0c73ec819080ab60a9e2f5f1f6 |
completed | May 3, 2026, 3:47 p.m. |
Created at: May 3, 2026, 4:05 p.m.