Triple

T12370086
Position Surface form Disambiguated ID Type / Status
Subject Progressive GAN E294977 entity
Predicate describedInPaper P519 FINISHED
Object Progressive Growing of GANs for Improved Quality, Stability, and Variation E294977 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: Progressive Growing of GANs for Improved Quality, Stability, and Variation | Statement: [Progressive GAN, describedInPaper, Progressive Growing of GANs for Improved Quality, Stability, and Variation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Progressive Growing of GANs for Improved Quality, Stability, and Variation
Context triple: [Progressive GAN, describedInPaper, Progressive Growing of GANs for Improved Quality, Stability, and Variation]
  • A. Progressive GAN chosen
    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.
  • B. Improved Techniques for Training GANs
    "Improved Techniques for Training GANs" is a 2016 research paper by Salimans et al. that proposes practical methods to stabilize and enhance Generative Adversarial Network training and introduces the Inception Score for evaluating generated images.
  • 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. StyleGAN
    StyleGAN is a state-of-the-art generative adversarial network architecture known for producing highly realistic, controllable images by manipulating disentangled style representations at different layers of the network.
  • E. Fréchet Inception Distance
    Fréchet Inception Distance is a widely used quantitative metric that measures the similarity between real and generated images by comparing their feature distributions extracted from a pretrained Inception network.
  • 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_69d6ab6d8a4081908636601e69ddf262 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93fa65a608190a1597a49751185a8 completed April 10, 2026, 6:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63473efd481909b2061f3b19e1aaf completed May 2, 2026, 5:29 p.m.
Created at: April 8, 2026, 9:54 p.m.