Triple

T12207436
Position Surface form Disambiguated ID Type / Status
Subject Wasserstein GAN E290870 entity
Predicate basedOn P98 FINISHED
Object Wasserstein distance
Wasserstein distance is a metric from optimal transport theory that measures the minimal “cost” of transforming one probability distribution into another, widely used to compare distributions in statistics and machine learning.
 E970674 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: Wasserstein distance | Statement: [Wasserstein GAN, basedOn, Wasserstein distance]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wasserstein distance
Context triple: [Wasserstein GAN, basedOn, Wasserstein distance]
  • A. 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.
  • B. Wasserstein GAN
    Wasserstein GAN is a variant of generative adversarial networks that improves training stability and sample quality by optimizing the Wasserstein (Earth Mover’s) distance between real and generated data distributions.
  • C. Optimal Transport: Old and New
    "Optimal Transport: Old and New" is a comprehensive monograph by Cédric Villani that develops the theory of optimal transport and its applications across analysis, geometry, and probability.
  • 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. 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. 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: Wasserstein distance
Triple: [Wasserstein GAN, basedOn, Wasserstein distance]
Generated description
Wasserstein distance is a metric from optimal transport theory that measures the minimal “cost” of transforming one probability distribution into another, widely used to compare distributions in statistics and machine learning.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wasserstein distance
Target entity description: Wasserstein distance is a metric from optimal transport theory that measures the minimal “cost” of transforming one probability distribution into another, widely used to compare distributions in statistics and machine learning.
  • A. 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.
  • B. Wasserstein GAN
    Wasserstein GAN is a variant of generative adversarial networks that improves training stability and sample quality by optimizing the Wasserstein (Earth Mover’s) distance between real and generated data distributions.
  • C. Optimal Transport: Old and New
    "Optimal Transport: Old and New" is a comprehensive monograph by Cédric Villani that develops the theory of optimal transport and its applications across analysis, geometry, and probability.
  • 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. 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. 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_69d6ab65923081909acfc61b7a612233 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91c7d8f5c8190a46e9caa2a920fa9 completed April 10, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a9d2f0c81908352cd9f0167c6ab completed May 2, 2026, 2:30 p.m.
NEDg Description generation batch_69f60f2154c8819081f9cf6f51e5255b completed May 2, 2026, 2:50 p.m.
NED2 Entity disambiguation (via description) batch_69f60fe9dd6c819092fa7bced2eea069 completed May 2, 2026, 2:53 p.m.
Created at: April 8, 2026, 9:51 p.m.