Triple
T4277076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RandomizedSearchCV |
E97068
|
entity |
| Predicate | inheritsFrom |
P3800
|
FINISHED |
| Object | BaseSearchCV |
E426667
|
NE FINISHED |
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: BaseSearchCV | Statement: [RandomizedSearchCV, inheritsFrom, BaseSearchCV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BaseSearchCV Context triple: [RandomizedSearchCV, inheritsFrom, BaseSearchCV]
-
A.
BaseSearchCV
chosen
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.
-
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.
RandomizedSearchCV
RandomizedSearchCV is a scikit-learn tool that performs hyperparameter optimization by randomly sampling parameter combinations and evaluating them via cross-validation.
-
D.
Neural Architecture Search
Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
-
E.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501ef1388190b0c968b069014a59 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c7237b608190ab5aca56027344c4 |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 12, 2026, 11:07 p.m.