Triple

T15361025
Position Surface form Disambiguated ID Type / Status
Subject Vladimir Vapnik E367287 entity
Predicate authorOf P4244 FINISHED
Object The Nature of Statistical Learning Theory E1153663 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: The Nature of Statistical Learning Theory | Statement: [Vladimir Vapnik, authorOf, The Nature of Statistical Learning Theory]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: The Nature of Statistical Learning Theory
Context triple: [Vladimir Vapnik, authorOf, The Nature of Statistical Learning Theory]
  • A. The Nature of Statistical Learning Theory chosen
    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.
  • B. 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.
  • 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. 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. Bayesian learning for neural networks
    Bayesian learning for neural networks is an approach that applies Bayesian inference to neural network models, treating their weights as probability distributions to improve uncertainty estimation and generalization.
  • 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_69ff1a6991148190b522684b35c07b1a completed May 9, 2026, 11:28 a.m.
Created at: April 10, 2026, 3:18 a.m.