Triple

T10038360
Position Surface form Disambiguated ID Type / Status
Subject Hellinger distance E205229 entity
Predicate relatedTo P37 FINISHED
Object Bhattacharyya distance E207203 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: Bhattacharyya distance | Statement: [Hellinger distance, relatedTo, Bhattacharyya distance]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bhattacharyya distance
Context triple: [Hellinger distance, relatedTo, Bhattacharyya distance]
  • A. Bhattacharyya distance chosen
    Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
  • B. 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.
  • 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. Kullback–Leibler divergence
    Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
  • E. 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.
  • 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_69ca834f70e88190b2d74828b7767ec1 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcee04afc8190904704d66e23a432 completed April 2, 2026, 2:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b60e988c8190839a088e65b15d07 completed April 5, 2026, 7:20 p.m.
Created at: March 30, 2026, 8:55 p.m.