Triple
T18178342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PyMC3 |
E435219
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Bayesian modeling framework |
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 modeling framework Context triple: [PyMC3, instanceOf, Bayesian modeling framework]
-
A.
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.
-
B.
statistical model
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
-
C.
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.
-
D.
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.
-
E.
machine learning framework
A machine learning framework is a software library or platform that provides tools, abstractions, and workflows to design, train, evaluate, and deploy machine learning models efficiently.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
Created at: April 10, 2026, 10:31 a.m.