Triple

T8577170
Position Surface form Disambiguated ID Type / Status
Subject PixelRNN E203075 entity
Predicate introducedInPaper P513 FINISHED
Object Pixel Recurrent Neural Networks 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: Pixel Recurrent Neural Networks | Statement: [PixelRNN, introducedInPaper, Pixel Recurrent Neural Networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pixel Recurrent Neural Networks
Context triple: [PixelRNN, introducedInPaper, Pixel Recurrent Neural Networks]
  • A. PixelRNN chosen
    PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
  • B. Generating sequences with recurrent neural networks
    "Generating Sequences with Recurrent Neural Networks" is a highly influential research paper by Alex Graves that advanced the use of RNNs for tasks like handwriting and text generation by demonstrating powerful sequence modeling and generation capabilities.
  • C. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
    "Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
  • D. PixelCNN
    PixelCNN is a deep generative model that uses convolutional neural networks with autoregressive masking to model and generate images pixel by pixel.
  • E. Sequence transduction with recurrent neural networks
    "Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
  • 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_69cbea97787481909ebbaa45f59cbdaa completed March 31, 2026, 3:39 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.