Triple

T18724960
Position Surface form Disambiguated ID Type / Status
Subject neural tangent kernel E457875 entity
Predicate mathematicallyRelatedTo P57352 FINISHED
Object Gaussian process limits of neural networks — 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: Gaussian process limits of neural networks | Statement: [neural tangent kernel, mathematicallyRelatedTo, Gaussian process limits of neural networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gaussian process limits of neural networks
Context triple: [neural tangent kernel, mathematicallyRelatedTo, Gaussian process limits of neural networks]
  • A. Intriguing properties of neural networks
    "Intriguing properties of neural networks" is a highly influential research paper that revealed surprising vulnerabilities and behaviors of deep neural networks, particularly their susceptibility to adversarial examples.
  • B. 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.
  • C. Gaussian process chosen
    A Gaussian process is a collection of random variables indexed by a set (often time or space) such that every finite subset has a joint multivariate normal distribution, widely used to model functions in probability theory and machine learning.
  • D. 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.
  • E. Randomized ReLU
    Randomized ReLU is a neural network activation function that introduces randomness into the slope of the negative part of the ReLU to improve robustness and generalization.
  • 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_69d8d393ba9c8190a8b03b04ddbb0a09 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e56d72d2c4819080b0d31860976b5e completed April 20, 2026, 12:04 a.m.
Created at: April 10, 2026, 11:50 a.m.