Triple

T15361038
Position Surface form Disambiguated ID Type / Status
Subject Vladimir Vapnik E367287 entity
Predicate hasConceptNamedAfter P3325 FINISHED
Object Vapnik–Chervonenkis classes E1154230 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: Vapnik–Chervonenkis classes | Statement: [Vladimir Vapnik, hasConceptNamedAfter, Vapnik–Chervonenkis classes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vapnik–Chervonenkis classes
Context triple: [Vladimir Vapnik, hasConceptNamedAfter, Vapnik–Chervonenkis classes]
  • A. Vapnik–Chervonenkis theory chosen
    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.
  • B. The Nature of Statistical Learning Theory
    The Nature of Statistical Learning Theory is a foundational book by Vladimir Vapnik that introduces the theoretical framework underlying modern statistical learning and support vector machines.
  • C. Probably Approximately Correct learning (PAC learning)
    Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
  • D. Computational Learning Theory
    Computational Learning Theory is a branch of computer science and mathematics that studies the design and analysis of algorithms that can learn patterns or functions from data, often using formal models of learning and complexity.
  • E. Cover’s theorem on the separability of patterns
    Cover’s theorem on the separability of patterns is a fundamental result in statistical learning theory stating that complex pattern-classification problems are more likely to be linearly separable when data are mapped into a higher-dimensional feature space.
  • 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_69ff219863b48190a68e73c85e27472c completed May 9, 2026, 11:59 a.m.
Created at: April 10, 2026, 3:18 a.m.