Triple

T19372960
Position Surface form Disambiguated ID Type / Status
Subject Vladimir Kotelnikov E484589 entity
Predicate hasConceptNamedAfter P3325 FINISHED
Object Kotelnikov condition in sampling — NE NERFINISHED

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: Kotelnikov condition in sampling | Statement: [Vladimir Kotelnikov, hasConceptNamedAfter, Kotelnikov condition in sampling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kotelnikov condition in sampling
Context triple: [Vladimir Kotelnikov, hasConceptNamedAfter, Kotelnikov condition in sampling]
  • A. Nyquist theorem chosen
    The Nyquist theorem is a fundamental principle in signal processing that states a continuous signal can be perfectly reconstructed from its samples if it is sampled at more than twice its highest frequency component.
  • B. Dirichlet conditions
    Dirichlet conditions are a set of sufficient criteria on a function—such as piecewise continuity and having a finite number of extrema and discontinuities on an interval—that guarantee the convergence of its Fourier series representation.
  • C. Paley–Wiener theorem
    The Paley–Wiener theorem is a fundamental result in harmonic analysis that characterizes which functions arise as Fourier transforms of compactly supported functions (or distributions), linking analytic properties of entire functions with support properties in the original domain.
  • D. Gibbs phenomenon
    The Gibbs phenomenon is the persistent overshoot and oscillation that occurs near jump discontinuities when approximating a function with its Fourier series or other truncated series expansions.
  • E. Wiener–Khinchin theorem
    The Wiener–Khinchin theorem is a fundamental result in signal processing and probability theory that relates a wide-sense stationary random process’s autocorrelation function to its power spectral density via the Fourier transform.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d305088190ad13571532aa454c completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e61a59f8cc8190b314cba95c25f2a0 completed April 20, 2026, 12:21 p.m.
Created at: April 10, 2026, 1:35 p.m.