Triple
T35689834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian learning for neural networks |
E1031257
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | probabilistic modeling technique |
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: probabilistic modeling technique Context triple: [Bayesian learning for neural networks, instanceOf, probabilistic modeling technique]
-
A.
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.
-
B.
parameter estimation technique
A parameter estimation technique is a systematic method used to infer the numerical values of unknown model parameters from observed data, typically by optimizing a chosen error or likelihood criterion.
-
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.
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.
-
E.
predictive model
A predictive model is a computational or mathematical representation that uses patterns in existing data to estimate or forecast unknown or future 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_69f76e0c73ec819080ab60a9e2f5f1f6 |
completed | May 3, 2026, 3:47 p.m. |
Created at: May 3, 2026, 4:05 p.m.