Triple
T27604995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayes factor |
E700155
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Bayesian model comparison metric |
C15490
|
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 model comparison metric Context triple: [Bayes factor, instanceOf, Bayesian model comparison metric]
-
A.
meta-estimator
A meta-estimator is a higher-level model that wraps or combines one or more base estimators to extend, modify, or coordinate their behavior for tasks like ensembling, preprocessing, or model selection.
-
B.
model selection utility
chosen
A model selection utility is a tool or component that evaluates and compares multiple candidate models using defined criteria to automatically choose the most suitable one for a given task or dataset.
-
C.
evaluation metric
An evaluation metric is a quantitative measure used to assess the performance, quality, or effectiveness of a model, system, or process against defined criteria or ground truth.
-
D.
likelihood function
A likelihood function is a mathematical function that measures how probable a set of observed data is for different values of a model’s parameters, treating the data as fixed and the parameters as variable.
-
E.
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.
- 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_69ef6a4e2e208190b63b7268f405785c |
completed | April 27, 2026, 1:53 p.m. |
Created at: April 27, 2026, 2:09 p.m.