Triple

T8728971
Position Surface form Disambiguated ID Type / Status
Subject Bhattacharyya distance E207203 entity
Predicate definedFrom P909 FINISHED
Object Bhattacharyya coefficient E753440 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 coefficient | Statement: [Bhattacharyya distance, definedFrom, Bhattacharyya coefficient]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bhattacharyya coefficient
Context triple: [Bhattacharyya distance, definedFrom, Bhattacharyya coefficient]
  • A. Bhattacharyya coefficient chosen
    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.
  • 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. Mahalanobis distance
    Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
  • E. 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.
  • 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_69cf42b0f5808190863a1ca3c4e9c8d1 completed April 3, 2026, 4:31 a.m.
Created at: March 30, 2026, 6:37 p.m.