Triple

T1836077
Position Surface form Disambiguated ID Type / Status
Subject Rényi divergence E41069 entity
Predicate generalizes P2372 FINISHED
Object Kullback–Leibler divergence E6392 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: Kullback–Leibler divergence | Statement: [Rényi divergence, generalizes, Kullback–Leibler divergence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kullback–Leibler divergence
Context triple: [Rényi divergence, generalizes, Kullback–Leibler divergence]
  • A. Kullback–Leibler divergence chosen
    Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
  • B. Rényi divergence
    Rényi divergence is a family of information-theoretic measures that generalize Kullback–Leibler divergence to quantify the dissimilarity between probability distributions, parameterized by an order α.
  • C. Bhattacharyya distance
    Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
  • D. Hellinger distance
    Hellinger distance is a statistical measure of dissimilarity between probability distributions, derived from the Euclidean distance between their square-root densities and widely used in probability theory and information geometry.
  • E. 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.
  • 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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb028226481908558c11449e1d6b6 completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adead6888081909f89704f0c070d68 completed March 8, 2026, 9:32 p.m.
Created at: March 4, 2026, 7:33 p.m.