Triple

T19231114
Position Surface form Disambiguated ID Type / Status
Subject Bernstein polynomials E480872 entity
Predicate usedToDefine P773 FINISHED
Object Bernstein approximation 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: Bernstein approximation | Statement: [Bernstein polynomials, usedToDefine, Bernstein approximation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bernstein approximation
Context triple: [Bernstein polynomials, usedToDefine, Bernstein approximation]
  • A. Bernstein polynomials chosen
    Bernstein polynomials are a family of polynomials used in approximation theory that provide a constructive proof of the Weierstrass approximation theorem by uniformly approximating continuous functions on a closed interval.
  • B. Weierstrass approximation theorem
    The Weierstrass approximation theorem is a fundamental result in real analysis stating that any continuous function on a closed interval can be uniformly approximated by polynomials.
  • C. Bernstein inequalities
    Bernstein inequalities are fundamental results in approximation theory and probability that provide bounds on the derivatives or deviations of functions and random variables under certain smoothness or moment conditions.
  • D. Chebyshev rational approximation
    Chebyshev rational approximation is a mathematical technique that uses rational functions to approximate other functions with near-optimal uniform accuracy over a given interval.
  • E. Chebyshev alternation theorem
    The Chebyshev alternation theorem is a fundamental result in approximation theory that characterizes the best uniform (minimax) polynomial approximation to a continuous function by the presence of alternating maximum errors at a finite set of points.
  • 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_69d8e8ccb8f48190ad420098e74fb1db completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5fa9ce5e081909df994841ce476d5 completed April 20, 2026, 10:06 a.m.
Created at: April 10, 2026, 1:25 p.m.