Triple

T12207438
Position Surface form Disambiguated ID Type / Status
Subject Wasserstein GAN E290870 entity
Predicate proposedInPaper P35415 FINISHED
Object Wasserstein GAN E290870 NE FINISHED

How this triple was built (3 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 GAN | Statement: [Wasserstein GAN, proposedInPaper, Wasserstein GAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wasserstein GAN
Context triple: [Wasserstein GAN, proposedInPaper, Wasserstein GAN]
  • A. Wasserstein GAN chosen
    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.
  • B. Conditional GAN
    A Conditional GAN is a type of generative adversarial network that produces data samples conditioned on auxiliary information such as class labels or input images, enabling controlled and targeted generation.
  • C. Progressive GAN
    Progressive GAN is a generative adversarial network architecture that grows both the generator and discriminator layers progressively during training to produce high-resolution, high-quality synthetic images.
  • D. Deep Convolutional GAN
    Deep Convolutional GAN is a widely used GAN architecture that replaces fully connected layers with deep convolutional layers to generate high-quality, realistic images.
  • E. Generative Adversarial Networks
    Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: proposedInPaper
Context triple: [Wasserstein GAN, proposedInPaper, Wasserstein GAN]
  • A. proposedInContextOf
    Indicates that something (such as an idea, action, or change) was proposed specifically within, and in relation to, a particular situation, setting, or surrounding circumstances.
  • B. proposedPart
    Indicates that one entity has been suggested or put forward to serve as a component or element of another entity.
  • C. proposedInYear
    Indicates that something, such as a plan, idea, or piece of legislation, was formally put forward or suggested in a specific calendar year.
  • D. publishedWhitePaperOn
    Indicates that an entity has authored and released a formal white paper on a specified topic or subject.
  • E. includedInPublication chosen
    Indicates that one entity (such as a work, section, or item) appears as part of, or is contained within, a specific publication.
  • F. None of above.

Provenance (4 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_69d920e312708190b4aede2e21f5f697 completed April 10, 2026, 4:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e5666f48190a28eed761e7b9210 completed May 2, 2026, 3:55 p.m.
PD Predicate disambiguation batch_69d91c3d669c81908eea7ad61122d275 completed April 10, 2026, 3:50 p.m.
Created at: April 8, 2026, 9:51 p.m.