Triple

T10060487
Position Surface form Disambiguated ID Type / Status
Subject Tsallis divergence E212974 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: [Tsallis divergence, generalizes, Kullback–Leibler divergence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kullback–Leibler divergence
Context triple: [Tsallis 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. Jensen–Shannon divergence
    Jensen–Shannon divergence is a symmetrized and smoothed measure of dissimilarity between probability distributions, widely used in information theory and machine learning.
  • D. Tsallis divergence
    Tsallis divergence is a generalized measure of statistical distance between probability distributions derived from Tsallis entropy, often used in nonextensive statistical mechanics and information theory.
  • E. 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.
  • 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_69ca83977128819084084eb7d1d8c52a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcfd0d23081909510785ef8a186a3 completed April 2, 2026, 2:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b624191c819093b8392b5573fa96 completed April 5, 2026, 7:21 p.m.
Created at: March 30, 2026, 8:57 p.m.