Triple
T22137950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergei Natanovich Bernstein |
E547080
|
entity |
| Predicate | notableConcept |
P201
|
FINISHED |
| Object | Bernstein inequalities |
—
|
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 inequalities | Statement: [Sergei Natanovich Bernstein, notableConcept, Bernstein inequalities]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernstein inequalities Context triple: [Sergei Natanovich Bernstein, notableConcept, Bernstein inequalities]
-
A.
Bernstein inequalities
chosen
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.
-
B.
Markov brothers' inequalities
Markov brothers' inequalities are classical results in approximation theory that provide upper bounds on the derivatives of polynomials in terms of their degree and maximum absolute value on an interval.
-
C.
Bernstein polynomials
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.
-
D.
Friedrichs inequality
Friedrichs inequality is a fundamental result in functional analysis and partial differential equations that provides bounds on the norms of functions in terms of their derivatives, playing a key role in the theory of Sobolev spaces and boundary value problems.
-
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 (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_69e11e3a95d88190a3bd80d9471976c3 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129bb78bc8190b74af3a5f6a5348f |
completed | April 28, 2026, 9:42 p.m. |
Created at: April 16, 2026, 8:32 p.m.