Triple

T307342
Position Surface form Disambiguated ID Type / Status
Subject DeepMind E6331 entity
Predicate developed P73 FINISHED
Object WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
 E39544 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: WaveNet | Statement: [DeepMind, developed, WaveNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WaveNet
Context triple: [DeepMind, developed, WaveNet]
  • A. GPT-2
    GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
  • B. GPT-3
    GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
  • C. Versoix
    Versoix is a Swiss municipality on the shores of Lake Geneva, known as a residential suburb of Geneva with lakeside promenades and a mix of urban and natural landscapes.
  • D. DALL·E
    DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
  • 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. 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: WaveNet
Triple: [DeepMind, developed, WaveNet]
Generated description
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WaveNet
Target entity description: WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
  • A. GPT-2
    GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
  • B. GPT-3
    GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
  • C. Versoix
    Versoix is a Swiss municipality on the shores of Lake Geneva, known as a residential suburb of Geneva with lakeside promenades and a mix of urban and natural landscapes.
  • D. DALL·E
    DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
  • 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. 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_69a2e79230508190b912ecb555aae17e completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ea313be88190b4441f3ea41a99e2 completed Feb. 28, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3b4744d188190a38831b251ca4901 completed March 1, 2026, 3:37 a.m.
NEDg Description generation batch_69a3b544d6b081908b11d83449f40e67 completed March 1, 2026, 3:40 a.m.
NED2 Entity disambiguation (via description) batch_69a3b5b7c9a48190afea4c39ab702fb9 completed March 1, 2026, 3:42 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.