Triple

T9245168
Position Surface form Disambiguated ID Type / Status
Subject Sander Dieleman E222172 entity
Predicate notableWork P4 FINISHED
Object Deep learning techniques for music recommendation (doctoral work)
"Deep learning techniques for music recommendation (doctoral work)" is Sander Dieleman’s PhD thesis that pioneered the application of deep neural networks to improve automated music recommendation and discovery.
E786387 NE FINISHED

How this triple was built (4 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 techniques for music recommendation (doctoral work) | Statement: [Sander Dieleman, notableWork, Deep learning techniques for music recommendation (doctoral work)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Deep learning techniques for music recommendation (doctoral work)
Context triple: [Sander Dieleman, notableWork, Deep learning techniques for music recommendation (doctoral work)]
  • A. Human Jukebox
    Human Jukebox is the renowned marching band of Southern University, celebrated for its high-energy performances, intricate formations, and influential role in HBCU band culture.
  • B. Deep Learning (book)
    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. The Well-Tempered Synthesizer
    The Well-Tempered Synthesizer is a pioneering 1969 electronic music album by Wendy Carlos that features Baroque and classical works performed on Moog synthesizers, helping to popularize synthesized music.
  • D. Music Genome Project
    The Music Genome Project is a comprehensive music analysis system that categorizes songs by hundreds of musical attributes to power personalized listening recommendations.
  • E. Neural Filters
    Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Deep learning techniques for music recommendation (doctoral work)
Triple: [Sander Dieleman, notableWork, Deep learning techniques for music recommendation (doctoral work)]
Generated description
"Deep learning techniques for music recommendation (doctoral work)" is Sander Dieleman’s PhD thesis that pioneered the application of deep neural networks to improve automated music recommendation and discovery.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Deep learning techniques for music recommendation (doctoral work)
Target entity description: "Deep learning techniques for music recommendation (doctoral work)" is Sander Dieleman’s PhD thesis that pioneered the application of deep neural networks to improve automated music recommendation and discovery.
  • A. Human Jukebox
    Human Jukebox is the renowned marching band of Southern University, celebrated for its high-energy performances, intricate formations, and influential role in HBCU band culture.
  • B. Deep Learning (book)
    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. The Well-Tempered Synthesizer
    The Well-Tempered Synthesizer is a pioneering 1969 electronic music album by Wendy Carlos that features Baroque and classical works performed on Moog synthesizers, helping to popularize synthesized music.
  • D. Music Genome Project
    The Music Genome Project is a comprehensive music analysis system that categorizes songs by hundreds of musical attributes to power personalized listening recommendations.
  • E. Neural Filters
    Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
  • F. None of above. chosen

Provenance (5 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_69ca83ee26cc81909ac624e190597d6d completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cd03efaa748190973916bd790f6e3a completed April 1, 2026, 11:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69d077f14804819098f443a2517ad461 completed April 4, 2026, 2:31 a.m.
NEDg Description generation batch_69d07933d26c81909257a4e6a5fe1c6e completed April 4, 2026, 2:36 a.m.
NED2 Entity disambiguation (via description) batch_69d079ed3eb48190b410934b809ebc3d completed April 4, 2026, 2:39 a.m.
Created at: March 30, 2026, 7:30 p.m.