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: Markov inequality | Statement: [Chebyshev inequalities, relatedTo, Markov inequality]

Target entity: Markov inequality
Context triple: [Chebyshev inequalities, relatedTo, Markov inequality]

• A. Chebyshev inequalities
  Chebyshev inequalities are probabilistic bounds that limit how much a random variable’s values can deviate from its mean in terms of its variance.
• B. Azuma–Hoeffding inequality
  The Azuma–Hoeffding inequality is a concentration inequality that bounds the probability of large deviations for martingales with bounded differences, generalizing Hoeffding’s inequality to dependent sequences.
• C. Bennett inequality
  Bennett inequality is a probabilistic bound that provides exponential tail estimates for sums of independent random variables, refining classical concentration inequalities like Bernstein’s.
• D. Chernoff bound
  The Chernoff bound is a probabilistic inequality that gives exponentially decreasing upper bounds on the tail probabilities of sums of independent random variables.
• E. Karamata's inequality
  Karamata's inequality is a fundamental result in majorization theory that generalizes several classical inequalities by comparing sums of convex (or concave) functions over majorized sequences.
• 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: Markov inequality
Triple: [Chebyshev inequalities, relatedTo, Markov inequality]

Markov inequality is a fundamental result in probability theory that provides an upper bound on the probability that a non-negative random variable exceeds a given value in terms of its expected value.

Target entity: Markov inequality
Target entity description: Markov inequality is a fundamental result in probability theory that provides an upper bound on the probability that a non-negative random variable exceeds a given value in terms of its expected value.

• A. Chebyshev inequalities
  Chebyshev inequalities are probabilistic bounds that limit how much a random variable’s values can deviate from its mean in terms of its variance.
• B. Azuma–Hoeffding inequality
  The Azuma–Hoeffding inequality is a concentration inequality that bounds the probability of large deviations for martingales with bounded differences, generalizing Hoeffding’s inequality to dependent sequences.
• C. Bennett inequality
  Bennett inequality is a probabilistic bound that provides exponential tail estimates for sums of independent random variables, refining classical concentration inequalities like Bernstein’s.
• D. Chernoff bound
  The Chernoff bound is a probabilistic inequality that gives exponentially decreasing upper bounds on the tail probabilities of sums of independent random variables.
• E. Karamata's inequality
  Karamata's inequality is a fundamental result in majorization theory that generalizes several classical inequalities by comparing sums of convex (or concave) functions over majorized sequences.
• F. None of above. chosen