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 triangular arrays | Statement: [Lindeberg–Feller central limit theorem, hasVersion, Lindeberg–Feller theorem for triangular arrays]

Target entity: Lindeberg–Feller theorem for triangular arrays
Context triple: [Lindeberg–Feller central limit theorem, hasVersion, Lindeberg–Feller theorem for triangular arrays]

• 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. Isserlis’ theorem in probability theory
  Isserlis’ theorem in probability theory is a result that expresses higher-order moments of jointly Gaussian random variables in terms of sums of products of their pairwise covariances.
• F. None of above.
• G. Unsure - the case is ambiguous/there is not enough information to decide.