Triple

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).

Phrase: Deep Learning (book) | Statement: [Yoshua Bengio, hasWritten, Deep Learning (book)]

Target entity: Deep Learning (book)
Context triple: [Yoshua Bengio, hasWritten, Deep Learning (book)]

• A. Deep Learning (book) chosen
  Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
• B. “A fast learning algorithm for deep belief nets”
  “A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
• C. Deep belief networks
  Deep belief networks are a class of deep generative neural network models composed of stacked layers of latent variables, typically built from restricted Boltzmann machines, used for unsupervised feature learning and representation.
• D. “Learning representations by back-propagating errors”
  “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
• E. Generative Adversarial Networks
  Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
• F. None of above.
• G. Unsure - the case is ambiguous/there is not enough information to decide.