Triple

T22202216
Position Surface form Disambiguated ID Type / Status
Subject parallel distributed processing E548708 entity
Predicate influenced P9 FINISHED
Object deep learning — NE NERFINISHED

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: deep learning | Statement: [parallel distributed processing, influenced, deep learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: deep learning
Context triple: [parallel distributed processing, influenced, deep learning]
  • A. DNN
    DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
  • B. Deep Learning (book) chosen
    Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
  • C. Machine Learning
    Machine Learning is a peer-reviewed scientific journal that publishes research on all aspects of machine learning and related artificial intelligence methods.
  • D. artificial intelligence
    Artificial intelligence is a field of computer science focused on creating systems that can perform tasks typically requiring human intelligence, such as learning, reasoning, and problem-solving.
  • E. deep feedforward networks
    Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e3ecc7c8190b5f94cd8f42e9d37 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12b24c6fc81909e6ae62564846bd1 completed April 28, 2026, 9:48 p.m.
Created at: April 16, 2026, 8:36 p.m.