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: Berry–Esseen bounds for dependent variables | Statement: [Berry–Esseen theorem, hasGeneralizations, Berry–Esseen bounds for dependent variables]

Target entity: Berry–Esseen bounds for dependent variables
Context triple: [Berry–Esseen theorem, hasGeneralizations, Berry–Esseen bounds for dependent variables]

• A. Berry–Esseen theorem chosen
  The Berry–Esseen theorem is a quantitative refinement of the central limit theorem that provides explicit bounds on the rate of convergence of normalized sums of independent random variables to the normal distribution.
• B. Lindeberg–Feller central limit theorem
  The Lindeberg–Feller central limit theorem is a general form of the central limit theorem that provides conditions under which sums of independent, not necessarily identically distributed random variables converge in distribution to a normal law.
• C. Statistical Independence in Probability, Analysis and Number Theory
  "Statistical Independence in Probability, Analysis and Number Theory" is a mathematical monograph by Mark Kac that explores the concept of independence across probability theory, real analysis, and number theory.
• D. Khinchin–Kahane type inequalities
  Khinchin–Kahane type inequalities are fundamental results in probability and functional analysis that bound moments or norms of random series (often with Rademacher or Gaussian coefficients) in terms of each other, providing powerful tools for studying the geometry of Banach spaces and random processes.
• E. Edgeworth expansion
  Edgeworth expansion is an asymptotic series that refines the central limit theorem by providing higher-order approximations to the distribution of normalized sums of random variables.
• F. None of above.
• G. Unsure - the case is ambiguous/there is not enough information to decide.