Triple

T8728982
Position Surface form Disambiguated ID Type / Status
Subject Bhattacharyya distance E207203 entity
Predicate relatedTo P37 FINISHED
Object Hellinger distance E205229 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: Hellinger distance | Statement: [Bhattacharyya distance, relatedTo, Hellinger distance]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hellinger distance
Context triple: [Bhattacharyya distance, relatedTo, Hellinger distance]
  • A. Hellinger distance chosen
    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.
  • B. 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.
  • C. Kullback–Leibler divergence
    Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
  • D. 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.
  • E. 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.
  • 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_69ca8358e4008190898471a59b96c301 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d19fdc88190860e0c9c93ab79ce completed March 31, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf2923abc48190a5b6027c2e4f1db7 completed April 3, 2026, 2:42 a.m.
Created at: March 30, 2026, 6:37 p.m.