Triple

T8912625
Position Surface form Disambiguated ID Type / Status
Subject Fisher information E212219 entity
Predicate appearsIn P795 FINISHED
Object Cramér–Rao inequality E157397 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
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).
Input
Phrase: Cramér–Rao inequality | Statement: [Fisher information, appearsIn, Cramér–Rao inequality]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cramér–Rao inequality
Context triple: [Fisher information, appearsIn, Cramér–Rao inequality]
  • A. Cramér–Rao bound chosen
    The Cramér–Rao bound is a fundamental result in statistical estimation theory that gives a lower limit on the variance of any unbiased estimator of a parameter, characterizing the best possible precision achievable.
  • B. Fisher information
    Fisher information is a fundamental concept in statistics that quantifies how much information an observable random variable carries about an unknown parameter, playing a key role in estimation theory and the Cramér–Rao bound.
  • C. Chernoff information
    Chernoff information is a measure in information theory and statistics that quantifies the exponential rate at which the error probability decays when optimally distinguishing between two probability distributions.
  • 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. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca8393b1808190bd4336787ffa2c40 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6525d1408190a76522d7c4ac37da completed April 1, 2026, 12:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc1cdfa6c8190af7b6312c73d59f9 completed April 3, 2026, 1:34 p.m.
Created at: March 30, 2026, 6:56 p.m.