Triple

T17520670
Position Surface form Disambiguated ID Type / Status
Subject Support Vector Machine E426671 entity
Predicate hasVariant P455 FINISHED
Object C-SVM — 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: C-SVM | Statement: [Support Vector Machine, hasVariant, C-SVM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: C-SVM
Context triple: [Support Vector Machine, hasVariant, C-SVM]
  • A. Svm
    Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
  • B. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • C. Support Vector Machines chosen
    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.
  • D. Vapnik–Chervonenkis theory
    Vapnik–Chervonenkis theory is a foundational framework in statistical learning that characterizes the capacity and generalization ability of learning algorithms through concepts like VC dimension.
  • 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 (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.