Triple

T11003650
Position Surface form Disambiguated ID Type / Status
Subject Neural Turing Machines E260059 entity
Predicate inspired P9 FINISHED
Object Differentiable Neural Computer E736824 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: Differentiable Neural Computer | Statement: [Neural Turing Machines, inspired, Differentiable Neural Computer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Differentiable Neural Computer
Context triple: [Neural Turing Machines, inspired, Differentiable Neural Computer]
  • A. Differentiable Neural Computers chosen
    Differentiable Neural Computers are a type of neural network architecture that augments traditional networks with an external, differentiable memory module, enabling them to learn algorithmic and reasoning tasks end-to-end.
  • B. Neural Turing Machines (contributions)
    Neural Turing Machines (contributions) refers to Oriol Vinyals’s work on augmenting neural networks with differentiable external memory to enable algorithmic reasoning and sequence learning beyond traditional architectures.
  • C. Neural Discrete Representation Learning
    Neural Discrete Representation Learning is a machine learning framework that introduces Vector Quantized Variational Autoencoders (VQ-VAE) to learn discrete latent representations for high-dimensional data such as images, audio, and video.
  • D. Sequence to Sequence Learning with Neural Networks
    "Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
  • E. Pointer Networks
    Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797553c408190b8dca21250243479 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
Created at: April 8, 2026, 9:25 p.m.