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: Shannon–Hartley theorem | Statement: [Nyquist theorem, relatedConcept, Shannon–Hartley theorem]

Target entity: Shannon–Hartley theorem
Context triple: [Nyquist theorem, relatedConcept, Shannon–Hartley theorem]

• A. Slepian–Wolf coding theorem
  The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
• B. noisy-channel coding theorem
  The noisy-channel coding theorem is a fundamental result in information theory that establishes the maximum rate at which information can be transmitted over a noisy communication channel with arbitrarily low error using appropriate encoding schemes.
• C. Nyquist theorem
  The Nyquist theorem is a fundamental principle in signal processing that states a continuous signal can be perfectly reconstructed from its samples if it is sampled at more than twice its highest frequency component.
• D. Fano inequality
  Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
• E. Shannon–Khinchin axioms
  The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
• 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: Shannon–Hartley theorem
Triple: [Nyquist theorem, relatedConcept, Shannon–Hartley theorem]

The Shannon–Hartley theorem is a fundamental result in information theory that quantifies the maximum error-free data transmission rate over a communication channel with a given bandwidth and signal-to-noise ratio.

Target entity: Shannon–Hartley theorem
Target entity description: The Shannon–Hartley theorem is a fundamental result in information theory that quantifies the maximum error-free data transmission rate over a communication channel with a given bandwidth and signal-to-noise ratio.

• A. Slepian–Wolf coding theorem
  The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
• B. noisy-channel coding theorem
  The noisy-channel coding theorem is a fundamental result in information theory that establishes the maximum rate at which information can be transmitted over a noisy communication channel with arbitrarily low error using appropriate encoding schemes.
• C. Nyquist theorem
  The Nyquist theorem is a fundamental principle in signal processing that states a continuous signal can be perfectly reconstructed from its samples if it is sampled at more than twice its highest frequency component.
• D. Fano inequality
  Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
• E. Shannon–Khinchin axioms
  The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
• F. None of above. chosen