Triple

T12207666
Position Surface form Disambiguated ID Type / Status
Subject Fréchet Inception Distance E290874 entity
Predicate describedIn P519 FINISHED
Object GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium E59296 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: GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium | Statement: [Fréchet Inception Distance, describedIn, GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Context triple: [Fréchet Inception Distance, describedIn, GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium]
  • A. Generative Adversarial Networks chosen
    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.
  • 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. 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.
  • 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. 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.
  • 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_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.
Created at: April 8, 2026, 9:51 p.m.