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: Lindeberg–Feller theorem for independent but not identically distributed sequences | Statement: [Lindeberg–Feller central limit theorem, hasVersion, Lindeberg–Feller theorem for independent but not identically distributed sequences]

Target entity: Lindeberg–Feller theorem for independent but not identically distributed sequences
Context triple: [Lindeberg–Feller central limit theorem, hasVersion, Lindeberg–Feller theorem for independent but not identically distributed sequences]

• A. Lindeberg–Feller central limit theorem chosen
  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.
• B. Limit Laws for Sums of Independent Random Variables
  Limit Laws for Sums of Independent Random Variables is a foundational mathematical work that systematically develops the theory of probability limit theorems, including results such as the law of large numbers and central limit behavior for sums of independent random variables.
• C. Berry–Esseen theorem
  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.
• D. Khinchin's law of the iterated logarithm
  Khinchin's law of the iterated logarithm is a fundamental result in probability theory that precisely characterizes the almost-sure fluctuations of partial sums of independent random variables on the scale of the square root of twice the product of their variance and the iterated logarithm of the sample size.
• E. Lyapunov central limit theorem
  The Lyapunov central limit theorem is a version of the central limit theorem that provides sufficient moment conditions under which the normalized sum of independent (not necessarily identically distributed) random variables converges in distribution to a normal law.
• F. None of above.
• G. Unsure - the case is ambiguous/there is not enough information to decide.