Triple

T197615
Position Surface form Disambiguated ID Type / Status
Subject Deep Learning (book) E4032 entity
Predicate title P38 FINISHED
Object Deep Learning E4032 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: Deep Learning | Statement: [Deep Learning (book), title, Deep Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Deep Learning
Context triple: [Deep Learning (book), title, Deep Learning]
  • A. 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.
  • B. “Learning representations by back-propagating errors”
    “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
  • C. Boltzmann machines
    Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
  • D. “A fast learning algorithm for deep belief nets”
    “A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
  • E. Google Brain
    Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
  • 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_69a254bca59881909a15e1496f1508c7 completed Feb. 28, 2026, 2:36 a.m.
NER Named-entity recognition batch_69a25bc96aa081908ef74c9827c9aa48 completed Feb. 28, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3161ddd388190954fd4b73b32a3b1 completed Feb. 28, 2026, 4:21 p.m.
Created at: Feb. 28, 2026, 2:44 a.m.