Triple

T29328351
Position Surface form Disambiguated ID Type / Status
Subject Row LSTM E743714 entity
Predicate instanceOf P0 FINISHED
Object recurrent neural network architecture 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: recurrent neural network architecture
Context triple: [Row LSTM, instanceOf, recurrent neural network architecture]
  • A. 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.
  • B. neural network design method
    A neural network design method is a systematic approach for selecting, structuring, and configuring neural network architectures and training procedures to solve specific computational or learning tasks.
  • C. scalable RL architecture
    A scalable RL architecture is a modular, distributed system design that efficiently trains and serves reinforcement learning agents across large state-action spaces, high data volumes, and many concurrent tasks or environments.
  • D. neural network component
    A neural network component is a modular unit—such as a layer, activation function, or connection pattern—that processes and transforms input data as part of a larger neural architecture to enable learning and inference.
  • E. neural networks conference
    A neural networks conference is a professional gathering where researchers, practitioners, and industry experts present, discuss, and collaborate on the latest advances, applications, and theories in neural network and deep learning technologies.
  • 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_69f09125f784819080f4e9fce9fe624f completed April 28, 2026, 10:51 a.m.
Created at: April 28, 2026, 1:28 p.m.