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: Show and Tell: A Neural Image Caption Generator | Statement: [Show and Tell: A Neural Image Caption Generator, title, Show and Tell: A Neural Image Caption Generator]

Target entity: Show and Tell: A Neural Image Caption Generator
Context triple: [Show and Tell: A Neural Image Caption Generator, title, Show and Tell: A Neural Image Caption Generator]

• A. Show and Tell: A Neural Image Caption Generator chosen
  "Show and Tell: A Neural Image Caption Generator" is a pioneering deep learning model that automatically generates natural-language descriptions for images by combining convolutional and recurrent neural networks.
• B. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
  "Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
• C. ImageNet Classification with Deep Convolutional Neural Networks
  "ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
• D. Sequence to Sequence Learning with Neural Networks
  "Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
• 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.