Triple

T8483181
Position Surface form Disambiguated ID Type / Status
Subject WaveGlow E200567 entity
Predicate basedOn P98 FINISHED
Object Glow
Glow is a generative flow-based model architecture used for high-quality image and audio synthesis through invertible transformations.
 E736216 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: Glow | Statement: [WaveGlow, basedOn, Glow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Glow
Context triple: [WaveGlow, basedOn, Glow]
  • A. Glow
    "Glow" is a song featured on Kelly Clarkson's holiday album "When Christmas Comes Around..." that showcases her soulful vocals in a festive, contemporary pop setting.
  • B. Glow
    "Glow" is a track by the artist Tasty, likely featuring an energetic, electronic-influenced sound characteristic of their music style.
  • C. Glitter
    Glitter is a 2001 musical romantic drama film starring Mariah Carey as an aspiring singer navigating love and the music industry in 1980s New York City.
  • D. Glitter
    "Glitter" is an introspective, genre-blending EP by 070 Shake that helped establish her as a distinctive voice in contemporary hip-hop and alternative R&B.
  • E. Glitz
    Glitz is a crime novel by Elmore Leonard that follows a tough Miami cop entangled with a vengeful ex-con and the seedy underworld of Atlantic City.
  • 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: Glow
Triple: [WaveGlow, basedOn, Glow]
Generated description
Glow is a generative flow-based model architecture used for high-quality image and audio synthesis through invertible transformations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Glow
Target entity description: Glow is a generative flow-based model architecture used for high-quality image and audio synthesis through invertible transformations.
  • A. Glow
    "Glow" is a song featured on Kelly Clarkson's holiday album "When Christmas Comes Around..." that showcases her soulful vocals in a festive, contemporary pop setting.
  • B. Glow
    "Glow" is a track by the artist Tasty, likely featuring an energetic, electronic-influenced sound characteristic of their music style.
  • C. Glitter
    Glitter is a 2001 musical romantic drama film starring Mariah Carey as an aspiring singer navigating love and the music industry in 1980s New York City.
  • D. Glitter
    "Glitter" is an introspective, genre-blending EP by 070 Shake that helped establish her as a distinctive voice in contemporary hip-hop and alternative R&B.
  • E. Glitz
    Glitz is a crime novel by Elmore Leonard that follows a tough Miami cop entangled with a vengeful ex-con and the seedy underworld of Atlantic City.
  • 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_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_69ce3a348a8481908a72c7ac15605022 completed April 2, 2026, 9:43 a.m.
NEDg Description generation batch_69ce3b56c3d881909468c3304e84cdb8 completed April 2, 2026, 9:48 a.m.
NED2 Entity disambiguation (via description) batch_69ce3c2579a08190af15d40d4bf7bb9f completed April 2, 2026, 9:51 a.m.
Created at: March 30, 2026, 6:12 p.m.