Triple

T1807258
Position Surface form Disambiguated ID Type / Status
Subject Solomon Kullback E40248 entity
Predicate hasConceptNamedAfter P3325 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: [Solomon Kullback, hasConceptNamedAfter, Kullback–Leibler divergence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kullback–Leibler divergence
Context triple: [Solomon Kullback, hasConceptNamedAfter, 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. 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.
  • D. 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.
  • E. Kolmogorov distance
    Kolmogorov distance is a statistical metric that measures the maximum difference between two cumulative distribution functions, commonly used to quantify convergence in distribution and in goodness-of-fit tests.
  • 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_69a88643a3388190a612f2ebe1fb29e7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa6598bd388190b5aa69b972cc8f0a completed March 6, 2026, 5:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1bd5f68819080a4fe06e3e1f76d completed March 8, 2026, 7:45 p.m.
Created at: March 4, 2026, 7:32 p.m.