Triple
T17520472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BaseSearchCV |
E426667
|
entity |
| Predicate | superclassOf |
P62912
|
FINISHED |
| Object | RandomizedSearchCV |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: RandomizedSearchCV | Statement: [BaseSearchCV, superclassOf, RandomizedSearchCV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RandomizedSearchCV Context triple: [BaseSearchCV, superclassOf, RandomizedSearchCV]
-
A.
RandomizedSearchCV
chosen
RandomizedSearchCV is a scikit-learn tool that performs hyperparameter optimization by randomly sampling parameter combinations and evaluating them via cross-validation.
-
B.
GridSearchCV
GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
-
C.
BaseSearchCV
BaseSearchCV is a scikit-learn base class that implements the core logic for hyperparameter search estimators, providing shared functionality for classes like GridSearchCV and RandomizedSearchCV.
-
D.
Bayesian optimization
Bayesian optimization is a sample-efficient global optimization strategy that uses probabilistic surrogate models, typically Gaussian processes, to optimize expensive black-box functions with as few evaluations as possible.
-
E.
RandomForestClassifier
RandomForestClassifier is a popular ensemble machine learning algorithm in scikit-learn that builds multiple decision trees and aggregates their predictions for robust classification.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
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. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.