Triple

T4277205
Position Surface form Disambiguated ID Type / Status
Subject SVC E97071 entity
Predicate implementedInLibrary P55160 FINISHED
Object scikit-learn E17661 NE FINISHED

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: scikit-learn | Statement: [SVC, implementedInLibrary, scikit-learn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: scikit-learn
Context triple: [SVC, implementedInLibrary, 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. 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.
  • D. Support Vector Machines
    Support Vector Machines are a class of supervised learning algorithms used primarily for classification and regression tasks, which work by finding the optimal separating hyperplane between data classes in a high-dimensional feature space.
  • E. Apache Mahout
    Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: implementedInLibrary
Context triple: [SVC, implementedInLibrary, scikit-learn]
  • A. implementedInLanguage
    Indicates that a piece of software or code is written using a particular programming language.
  • B. hasOfficialLibrary
    Indicates that an entity possesses or is served by a formally recognized, authorized library.
  • C. canBeImplementedWith
    Indicates that one entity is capable of being realized, executed, or fulfilled through the use or application of another entity.
  • D. implementedOn
    Indicates that something (such as a feature, standard, or specification) is realized, executed, or put into effect within or on a particular platform, system, or environment.
  • E. commonlyImplementedBy
    Indicates that the referenced item (e.g., a standard, interface, or pattern) is frequently realized or put into practice by the associated implementing entities.
  • F. None of above. chosen

Provenance (5 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_69b5e4e7a7cc8190a2ffc15c236f80d5 completed March 14, 2026, 10:44 p.m.
PD Predicate disambiguation batch_69b347faa45481908c19c29fb906dc92 completed March 12, 2026, 11:10 p.m.
PDg Predicate description generation batch_69b34e0606488190baadf469a1afc3c2 completed March 12, 2026, 11:36 p.m.
Created at: March 12, 2026, 11:07 p.m.