Triple

T17521739
Position Surface form Disambiguated ID Type / Status
Subject Bioconductor E426691 entity
Predicate hasComponent P35 FINISHED
Object BiocSklearn package — 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: BiocSklearn package | Statement: [Bioconductor, hasComponent, BiocSklearn package]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BiocSklearn package
Context triple: [Bioconductor, hasComponent, BiocSklearn package]
  • A. 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.
  • B. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • C. Create ML
    Create ML is Apple's machine learning tool that lets developers easily build and train models directly on macOS using simple, user-friendly interfaces.
  • D. Bioconductor
    Bioconductor is an open-source project that provides R packages and tools for the analysis and comprehension of high-throughput genomic and biological data.
  • E. XGBoost
    XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
  • 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: BiocSklearn package
Target entity description: BiocSklearn package is a Bioconductor tool that integrates scikit-learn–style machine learning methods into the R/Bioconductor ecosystem for analyzing biological and genomic data.
  • A. 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.
  • B. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • C. Create ML
    Create ML is Apple's machine learning tool that lets developers easily build and train models directly on macOS using simple, user-friendly interfaces.
  • D. Bioconductor chosen
    Bioconductor is an open-source project that provides R packages and tools for the analysis and comprehension of high-throughput genomic and biological data.
  • E. XGBoost
    XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
  • F. None of above.

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_69e452d2f79881909556894728e255ab completed April 19, 2026, 3:58 a.m.
Created at: April 10, 2026, 5:49 a.m.