Triple

T8577179
Position Surface form Disambiguated ID Type / Status
Subject PixelRNN E203075 entity
Predicate architectureVariant P24956 FINISHED
Object Multi-Scale PixelRNN E203075 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: Multi-Scale PixelRNN | Statement: [PixelRNN, architectureVariant, Multi-Scale PixelRNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Multi-Scale PixelRNN
Context triple: [PixelRNN, architectureVariant, Multi-Scale PixelRNN]
  • A. PixelRNN chosen
    PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
  • B. PixelCNN
    PixelCNN is a deep generative model that uses convolutional neural networks with autoregressive masking to model and generate images pixel by pixel.
  • C. 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.
  • D. 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.
  • 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_69ca8328ebe481909a8c038fa79959b4 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc457ab8b08190a53c730417288deb completed March 31, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce899dd7d48190b44338b92ad68bd0 completed April 2, 2026, 3:22 p.m.
Created at: March 30, 2026, 6:22 p.m.