Triple

T7723781
Position Surface form Disambiguated ID Type / Status
Subject Thermal noise and Johnson–Nyquist noise theory E175077 entity
Predicate relatedTo P37 FINISHED
Object Nyquist theorem E166675 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: Nyquist theorem | Statement: [Thermal noise and Johnson–Nyquist noise theory, relatedTo, Nyquist theorem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nyquist theorem
Context triple: [Thermal noise and Johnson–Nyquist noise theory, relatedTo, Nyquist theorem]
  • 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. Nyquist
    Nyquist is a surname most famously associated with Swedish-American engineer Harry Nyquist, known for his foundational contributions to information theory and telecommunications.
  • C. Shannon–Hartley theorem
    The Shannon–Hartley theorem is a fundamental result in information theory that quantifies the maximum error-free data transmission rate over a communication channel with a given bandwidth and signal-to-noise ratio.
  • D. 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.
  • E. Parseval's theorem
    Parseval's theorem is a fundamental result in Fourier analysis that equates the total energy of a function in the time (or spatial) domain with the total energy of its representation in the frequency domain.
  • 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_69c6995d541c81909eaa646b1a8369a9 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c702f39fa48190b7b8a09446b5cf78 completed March 27, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8e581d4e881908c88d55364a5e014 completed March 29, 2026, 8:40 a.m.
Created at: March 27, 2026, 4:05 p.m.