Triple

T15360994
Position Surface form Disambiguated ID Type / Status
Subject Vladimir Vapnik E367287 entity
Predicate knownFor P22 FINISHED
Object support vector machines E426671 NE FINISHED

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: support vector machines | Statement: [Vladimir Vapnik, knownFor, support vector machines]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: support vector machines
Context triple: [Vladimir Vapnik, knownFor, support vector machines]
  • A. 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.
  • B. Svm
    Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
  • C. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • D. 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.
  • E. ML
    ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4607408190ab281a7f7a8012d3 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff0b4a181c8190bffc1ac1a86e215d completed May 9, 2026, 10:24 a.m.
Created at: April 10, 2026, 3:18 a.m.