Triple

T36489731
Position Surface form Disambiguated ID Type / Status
Subject recurrent neural networks E899021 entity
Predicate instanceOf P0 FINISHED
Object sequence model C11476 CONCEPT FINISHED

How this triple was built (1 step)

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.

CD Concept disambiguation gpt-5-mini-2025-08-07 
Target class: sequence model
Context triple: [recurrent neural networks, instanceOf, sequence model]
  • A. hierarchical transformer model
    A hierarchical transformer model is a neural network architecture that processes data at multiple levels of granularity (e.g., tokens, sentences, documents) using stacked transformer layers to capture both local and global contextual dependencies efficiently.
  • B. recurrent artificial neural network chosen
    A recurrent artificial neural network is a type of neural network where connections form directed cycles, allowing information to persist over time and enabling the modeling of sequential or temporal data.
  • C. former model
    A former model is an individual who previously worked professionally in modeling but has since left the industry or no longer does it as their primary occupation.
  • D. sigma model
    A sigma model is a quantum field theory in which fields map spacetime into a target manifold, with dynamics governed by the geometry of that manifold.
  • E. deep learning model
    A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
  • F. None of above.

Provenance (1 batch)

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_69f76e5ad4588190bdbce60c52fbb785 completed May 3, 2026, 3:48 p.m.
Created at: May 3, 2026, 4:10 p.m.