Triple

T8728970
Position Surface form Disambiguated ID Type / Status
Subject Bhattacharyya distance E207203 entity
Predicate relatedConcept P37 FINISHED
Object Bhattacharyya coefficient
The Bhattacharyya coefficient is a statistical measure of similarity between two probability distributions, often used to quantify their overlap in fields like pattern recognition and signal processing.
 E753440 NE FINISHED

How this triple was built (4 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: Bhattacharyya coefficient | Statement: [Bhattacharyya distance, relatedConcept, Bhattacharyya coefficient]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bhattacharyya coefficient
Context triple: [Bhattacharyya distance, relatedConcept, Bhattacharyya coefficient]
  • A. 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.
  • B. Kullback–Leibler divergence
    Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
  • 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. 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. 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. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bhattacharyya coefficient
Triple: [Bhattacharyya distance, relatedConcept, Bhattacharyya coefficient]
Generated description
The Bhattacharyya coefficient is a statistical measure of similarity between two probability distributions, often used to quantify their overlap in fields like pattern recognition and signal processing.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bhattacharyya coefficient
Target entity description: The Bhattacharyya coefficient is a statistical measure of similarity between two probability distributions, often used to quantify their overlap in fields like pattern recognition and signal processing.
  • A. 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.
  • B. Kullback–Leibler divergence
    Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
  • 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. 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. 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. chosen

Provenance (5 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.
NEDg Description generation batch_69cf2bd42e6081908e016303eeb2241f completed April 3, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69cf2ce47b748190b883063dc3e5d16b completed April 3, 2026, 2:58 a.m.
Created at: March 30, 2026, 6:37 p.m.