Triple

T12267239
Position Surface form Disambiguated ID Type / Status
Subject Conditional GAN E292378 entity
Predicate introducedInPaper P513 FINISHED
Object Conditional Generative Adversarial Nets E292378 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: Conditional Generative Adversarial Nets | Statement: [Conditional GAN, introducedInPaper, Conditional Generative Adversarial Nets]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Conditional Generative Adversarial Nets
Context triple: [Conditional GAN, introducedInPaper, Conditional Generative Adversarial Nets]
  • A. Conditional GAN chosen
    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.
  • B. 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.
  • C. 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.
  • 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_69d6ab6856488190b5d31178d5015f8e completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91cdd7b3c8190afd237cd9b633d4d completed April 10, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63461cacc81909958fbd745e5d065 completed May 2, 2026, 5:29 p.m.
Created at: April 8, 2026, 9:52 p.m.