Triple

T13614675
Position Surface form Disambiguated ID Type / Status
Subject Lectures on Ergodic Theory E325280 entity
Predicate subject P450 FINISHED
Object Kolmogorov–Sinai entropy E695939 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: Kolmogorov–Sinai entropy | Statement: [Lectures on Ergodic Theory, subject, Kolmogorov–Sinai entropy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kolmogorov–Sinai entropy
Context triple: [Lectures on Ergodic Theory, subject, Kolmogorov–Sinai entropy]
  • A. Kolmogorov–Sinai entropy chosen
    Kolmogorov–Sinai entropy is a fundamental invariant in dynamical systems theory that quantifies the average rate of information production or unpredictability of a measure-preserving transformation.
  • B. Rényi entropy
    Rényi entropy is a generalized measure of information and uncertainty that extends Shannon entropy by introducing a tunable order parameter to emphasize different aspects of a probability distribution.
  • C. Lyapunov exponents
    Lyapunov exponents are quantitative measures in dynamical systems theory that characterize the rates at which nearby trajectories diverge or converge, indicating the presence and strength of chaos.
  • D. Shannon entropy
    Shannon entropy is a fundamental measure in information theory that quantifies the average uncertainty or information content in a random variable or message source.
  • E. Khinchin–Kolmogorov theorem
    The Khinchin–Kolmogorov theorem is a fundamental result in probability theory that provides conditions under which series of independent random variables converge almost surely.
  • 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0ad0a7c81909c7972187202db96 completed April 12, 2026, 2:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77f9cbc388190972e949324144d2f completed May 3, 2026, 5:02 p.m.
Created at: April 9, 2026, 9:50 p.m.