Triple

T18724950
Position Surface form Disambiguated ID Type / Status
Subject neural tangent kernel E457875 entity
Predicate definedIn P775 FINISHED
Object “Neural Tangent Kernel: Convergence and Generalization in 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: “Neural Tangent Kernel: Convergence and Generalization in Neural Networks” | Statement: [neural tangent kernel, definedIn, “Neural Tangent Kernel: Convergence and Generalization in Neural Networks”]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: “Neural Tangent Kernel: Convergence and Generalization in Neural Networks”
Context triple: [neural tangent kernel, definedIn, “Neural Tangent Kernel: Convergence and Generalization in Neural Networks”]
  • A. neural tangent kernel chosen
    The neural tangent kernel is a theoretical construct that characterizes the training dynamics and generalization of infinitely wide neural networks by relating gradient descent to kernel methods.
  • B. 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.
  • C. 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.
  • D. Adam: A Method for Stochastic Optimization
    "Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
  • E. “Stochastic Gradient Descent Tricks”
    “Stochastic Gradient Descent Tricks” is a well-known paper by Léon Bottou that surveys practical techniques and heuristics for effectively applying stochastic gradient descent in machine learning.
  • 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.