Triple

T17520508
Position Surface form Disambiguated ID Type / Status
Subject BaseSearchCV E426667 entity
Predicate inheritsFrom P3800 FINISHED
Object sklearn.base.BaseEstimator — NE NERFINISHED

How this triple was built (3 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: sklearn.base.BaseEstimator | Statement: [BaseSearchCV, inheritsFrom, sklearn.base.BaseEstimator]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: sklearn.base.BaseEstimator
Context triple: [BaseSearchCV, inheritsFrom, sklearn.base.BaseEstimator]
  • A. 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.
  • B. 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.
  • C. GridSearchCV
    GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
  • D. LogisticRegression
    LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
  • E. TensorFlow Estimators
    TensorFlow Estimators are a high-level TensorFlow API that simplifies building, training, and deploying machine learning models with standardized workflows and production-ready features.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: sklearn.base.BaseEstimator
Target entity description: sklearn.base.BaseEstimator is the fundamental scikit-learn mixin class that provides core functionality for estimators, including standardized parameter handling, cloning, and introspection.
  • A. 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.
  • B. 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.
  • C. GridSearchCV
    GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
  • D. LogisticRegression
    LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
  • E. TensorFlow Estimators
    TensorFlow Estimators are a high-level TensorFlow API that simplifies building, training, and deploying machine learning models with standardized workflows and production-ready features.
  • F. None of above. chosen

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.