Triple

T2703838
Position Surface form Disambiguated ID Type / Status
Subject Generative Adversarial Networks E59296 entity
Predicate abbreviation P43 FINISHED
Object GANs 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 | Statement: [Generative Adversarial Networks, abbreviation, GANs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GANs
Context triple: [Generative Adversarial Networks, abbreviation, GANs]
  • 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. PixelCNN
    PixelCNN is a deep generative model that uses convolutional neural networks with autoregressive masking to model and generate images pixel by pixel.
  • C. PixelRNN
    PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
  • D. variational autoencoders
    Variational autoencoders are a class of generative neural networks that learn probabilistic latent representations of data, enabling them to generate new, similar samples.
  • E. Automatic Adam
    Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
  • 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_69ab4ac66bc88190b9e4afa5fc843f72 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abda5011bc8190ae4e41da391e759c completed March 7, 2026, 7:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf76caec8190930ead7931f7ea91 completed March 10, 2026, 5:43 a.m.
Created at: March 6, 2026, 9:55 p.m.