Triple
T36489682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian optimization |
E899020
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | global optimization method |
C33139
|
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: global optimization method Context triple: [Bayesian optimization, instanceOf, global optimization method]
-
A.
optimization paradigm
chosen
An optimization paradigm is a conceptual framework that defines how to formulate, search for, and evaluate solutions to a problem in order to find the best (or sufficiently good) outcome under given constraints and objectives.
-
B.
stochastic approximation method
A stochastic approximation method is an iterative algorithmic technique for finding roots or optima of functions when only noisy or sample-based observations are available, updating estimates using random data to converge to the desired solution.
-
C.
hyperparameter optimization tool
A hyperparameter optimization tool is a system that automatically searches, evaluates, and selects the best hyperparameter configurations to improve the performance of machine learning models.
-
D.
combinatorial optimization problem
A combinatorial optimization problem is a mathematical task of finding an optimal object (such as a subset, sequence, or arrangement) from a finite but typically large set of discrete possibilities, subject to given constraints.
-
E.
geometric optimization problem
A geometric optimization problem is a mathematical task that involves finding the best (e.g., shortest, largest, or most efficient) geometric configuration or measurement under given constraints.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:10 p.m.