Triple

T23142654
Position Surface form Disambiguated ID Type / Status
Subject Naive Bayes classifier E577500 entity
Predicate commonImplementation P3697 FINISHED
Object scikit-learn 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: scikit-learn | Statement: [Naive Bayes classifier, commonImplementation, scikit-learn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: scikit-learn
Context triple: [Naive Bayes classifier, commonImplementation, scikit-learn]
  • A. scikit-learn chosen
    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.
  • B. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • C. LIBLINEAR
    LIBLINEAR is an open-source machine learning library specialized for large-scale linear classification and regression, particularly efficient for linear SVMs and logistic regression.
  • D. RandomForestClassifier
    RandomForestClassifier is a popular ensemble machine learning algorithm in scikit-learn that builds multiple decision trees and aggregates their predictions for robust classification.
  • E. 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.
  • 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_69e245f8e6248190ba3d58e068b4dccb completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18ecb72fc8190a24e8f5756217a36 completed April 29, 2026, 4:53 a.m.
Created at: April 17, 2026, 4 p.m.