Triple

T8482875
Position Surface form Disambiguated ID Type / Status
Subject Alex Graves E200559 entity
Predicate coAuthorOf P2389 FINISHED
Object Differentiable Neural Computers paper 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 Computers paper | Statement: [Alex Graves, coAuthorOf, Differentiable Neural Computers paper]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Differentiable Neural Computers paper
Context triple: [Alex Graves, coAuthorOf, Differentiable Neural Computers paper]
  • 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. 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.
  • D. 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.
  • E. Connectionist Temporal Classification
    Connectionist Temporal Classification is a neural network training algorithm designed for sequence labeling tasks where input and output lengths differ and alignments are unknown, widely used in speech and handwriting recognition.
  • 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_69ca831b17988190a1f3f3413d57b820 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe53845e881909eeb32863c7aa942 completed March 31, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6d0a3abc8190a6fde29e728f15fe completed April 2, 2026, 1:20 p.m.
Created at: March 30, 2026, 6:12 p.m.