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

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

• A. Inception Score
  Inception Score is a quantitative metric used to assess the quality and diversity of images generated by generative models by analyzing their classifiability and distribution across categories using a pretrained Inception network.
• B. CLIP
  CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
• C. MRPC
  MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
• D. Maximal Marginal Relevance (MMR) for information retrieval and summarization
  Maximal Marginal Relevance (MMR) is an information retrieval and summarization technique that selects results by jointly maximizing relevance to a query while minimizing redundancy among the chosen items.
• E. Fréchet Inception Distance
  Fréchet Inception Distance is a widely used quantitative metric that measures the similarity between real and generated images by comparing their feature distributions extracted from a pretrained Inception network.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Entity: CIDEr
Triple: [Show and Tell: A Neural Image Caption Generator, evaluationMetric, CIDEr]

CIDEr is an automatic evaluation metric designed to assess the quality of image captions by measuring their consensus with human-written descriptions.

Target entity: CIDEr
Target entity description: CIDEr is an automatic evaluation metric designed to assess the quality of image captions by measuring their consensus with human-written descriptions.

• A. Inception Score
  Inception Score is a quantitative metric used to assess the quality and diversity of images generated by generative models by analyzing their classifiability and distribution across categories using a pretrained Inception network.
• B. CLIP
  CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
• C. MRPC
  MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
• D. Maximal Marginal Relevance (MMR) for information retrieval and summarization
  Maximal Marginal Relevance (MMR) is an information retrieval and summarization technique that selects results by jointly maximizing relevance to a query while minimizing redundancy among the chosen items.
• E. Fréchet Inception Distance
  Fréchet Inception Distance is a widely used quantitative metric that measures the similarity between real and generated images by comparing their feature distributions extracted from a pretrained Inception network.
• F. None of above. chosen