Triple

T12282803
Position Surface form Disambiguated ID Type / Status
Subject Wiener measure E292753 entity
Predicate associatedWith P37 FINISHED
Object Cameron–Martin space E59985 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: Cameron–Martin space | Statement: [Wiener measure, associatedWith, Cameron–Martin space]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cameron–Martin space
Context triple: [Wiener measure, associatedWith, Cameron–Martin space]
  • A. Cameron–Martin theorem chosen
    The Cameron–Martin theorem is a fundamental result in probability theory and functional analysis that characterizes how Gaussian measures on infinite-dimensional spaces change under shifts by elements of a special Hilbert subspace (the Cameron–Martin space).
  • B. Montel space
    A Montel space is a type of locally convex topological vector space in which every closed and bounded set is compact, implying strong convergence and compactness properties useful in functional analysis and distribution theory.
  • C. Schwartz space
    Schwartz space is the function space of rapidly decreasing smooth functions on Euclidean space, fundamental in distribution theory and Fourier analysis.
  • D. Sobolev spaces
    Sobolev spaces are function spaces that incorporate both functions and their weak derivatives, providing a fundamental framework for studying partial differential equations and variational problems.
  • E. Malliavin calculus
    Malliavin calculus is a branch of stochastic analysis that extends differential calculus to functionals of stochastic processes, particularly Brownian motion, enabling probabilistic proofs of regularity and smoothness for solutions to stochastic differential equations.
  • 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_69d6ab690ad081908c0ed3870ec82d53 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91cf2b09c81908a11581d33f65be0 completed April 10, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e70dec8819098199fbb54d888c1 completed May 2, 2026, 3:55 p.m.
Created at: April 8, 2026, 9:52 p.m.