Triple
T13266956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirichlet process models |
E315947
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Bayesian nonparametric model |
C26339
|
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 nonparametric model Context triple: [Dirichlet process models, instanceOf, Bayesian nonparametric model]
-
A.
statistical model
chosen
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
-
B.
seminal work in nonparametric statistics
A seminal work in nonparametric statistics is a foundational contribution that introduces or rigorously develops distribution-free methods for inference, estimation, or testing, significantly shaping subsequent theory and applications in the field.
-
C.
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.
-
D.
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.
-
E.
statistical inference method
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.
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
Created at: April 9, 2026, 9:25 p.m.