Triple

T4092180
Position Surface form Disambiguated ID Type / Status
Subject Jensen's inequality E87727 entity
Predicate usedFor P98 FINISHED
Object Kullback–Leibler divergence inequalities 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 inequalities | Statement: [Jensen's inequality, usedFor, Kullback–Leibler divergence inequalities]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kullback–Leibler divergence inequalities
Context triple: [Jensen's inequality, usedFor, Kullback–Leibler divergence inequalities]
  • A. 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.
  • B. Kullback–Leibler divergence chosen
    Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
  • C. 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.
  • D. Chernoff information
    Chernoff information is a measure in information theory and statistics that quantifies the exponential rate at which the error probability decays when optimally distinguishing between two probability distributions.
  • 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_69aed94425148190be337845d56fac22 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefcae22a081908af65a960306b78c completed March 9, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b6cfb288190ac08c3a37327ac9a completed March 14, 2026, 2:06 p.m.
Created at: March 9, 2026, 3:40 p.m.