Triple

T6858797
Position Surface form Disambiguated ID Type / Status
Subject Extrapolation, Interpolation, and Smoothing of Stationary Time Series E158219 entity
Predicate relatedConcept P37 FINISHED
Object Wiener–Kolmogorov prediction theory E158221 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: Wiener–Kolmogorov prediction theory | Statement: [Extrapolation, Interpolation, and Smoothing of Stationary Time Series, relatedConcept, Wiener–Kolmogorov prediction theory]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wiener–Kolmogorov prediction theory
Context triple: [Extrapolation, Interpolation, and Smoothing of Stationary Time Series, relatedConcept, Wiener–Kolmogorov prediction theory]
  • A. “A New Approach to Linear Filtering and Prediction Problems”
    “A New Approach to Linear Filtering and Prediction Problems” is Rudolf E. Kálmán’s landmark 1960 paper that introduced the Kalman filter, a foundational algorithm for optimal estimation in control theory, signal processing, and navigation.
  • B. Extrapolation, Interpolation, and Smoothing of Stationary Time Series
    "Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
  • C. Wiener filter chosen
    The Wiener filter is a signal processing technique that optimally estimates a desired signal from noisy observations by minimizing the mean square error, based on statistical properties of signal and noise.
  • D. Kailath factorization in linear systems
    Kailath factorization in linear systems is a matrix factorization technique used in control and signal processing to efficiently analyze and solve linear dynamical systems.
  • E. Neyman–Pearson theory of hypothesis testing
    The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
  • 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_69c68830cdbc8190a8301c7a9d9f651a completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d8720bd48190adb446130a03d2bf completed March 27, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c72fe79af081909baacbfd4d5e8f24 completed March 28, 2026, 1:33 a.m.
Created at: March 27, 2026, 2:21 p.m.