Triple
T17520466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BaseSearchCV |
E426667
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | hyperparameter search base class |
C15488
|
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: hyperparameter search base class Context triple: [BaseSearchCV, instanceOf, hyperparameter search base class]
-
A.
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.
-
B.
parameter space
The parameter space is the set of all possible values that a model’s parameters can take, defining the domain over which the model can vary or be optimized.
-
C.
scikit-learn class
chosen
A scikit-learn class is a Python object that encapsulates a specific machine learning component (such as an estimator, transformer, or model selection tool) with a consistent API for fitting to data and making predictions or transformations.
-
D.
machine learning model class
A machine learning model class is a blueprint that defines the structure, parameters, and learning behavior of models that can be instantiated to learn patterns from data and make predictions or decisions.
-
E.
optimization paradigm
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.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:49 a.m.