Triple

T12671720
Position Surface form Disambiguated ID Type / Status
Subject Radio E302699 entity
Predicate hasTrack P3284 FINISHED
Object Blue Velvet Rain
"Blue Velvet Rain" is a song featured on the music streaming and recommendation service Radio.
 E995583 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: Blue Velvet Rain | Statement: [Radio, hasTrack, Blue Velvet Rain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Blue Velvet Rain
Context triple: [Radio, hasTrack, Blue Velvet Rain]
  • A. Blue Velvet
    Blue Velvet is a 1986 neo-noir mystery film directed by David Lynch, renowned for its disturbing exploration of suburban darkness and surreal, psychologically intense atmosphere.
  • B. In the Rain
    "In the Rain" is a song best known as a soulful 1971 hit by the American R&B group The Dramatics, featuring lush production and emotive vocals.
  • C. Greasy Lake
    Greasy Lake is a fictional, seedy nighttime hangout spot depicted in Bruce Springsteen’s song “Spirit in the Night,” symbolizing youthful rebellion and wild escapades.
  • D. Velvet
    Velvet is a spy thriller comic book series written by Ed Brubaker that follows a veteran female secret agent drawn back into the world of espionage.
  • E. Velvet
    Velvet is a Spanish romantic drama television series set in a 1950s fashion house, focusing on the love story between a seamstress and the heir to the business.
  • 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: Blue Velvet Rain
Triple: [Radio, hasTrack, Blue Velvet Rain]
Generated description
"Blue Velvet Rain" is a song featured on the music streaming and recommendation service Radio.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Blue Velvet Rain
Target entity description: "Blue Velvet Rain" is a song featured on the music streaming and recommendation service Radio.
  • A. Blue Velvet
    Blue Velvet is a 1986 neo-noir mystery film directed by David Lynch, renowned for its disturbing exploration of suburban darkness and surreal, psychologically intense atmosphere.
  • B. In the Rain
    "In the Rain" is a song best known as a soulful 1971 hit by the American R&B group The Dramatics, featuring lush production and emotive vocals.
  • C. Greasy Lake
    Greasy Lake is a fictional, seedy nighttime hangout spot depicted in Bruce Springsteen’s song “Spirit in the Night,” symbolizing youthful rebellion and wild escapades.
  • D. Velvet
    Velvet is a spy thriller comic book series written by Ed Brubaker that follows a veteran female secret agent drawn back into the world of espionage.
  • E. Velvet
    Velvet is a Spanish romantic drama television series set in a 1950s fashion house, focusing on the love story between a seamstress and the heir to the business.
  • 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_69d7bdee64a08190801c6d470aefd723 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d961ae493481908f82e0d05dce20bd completed April 10, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6689019988190ae3a3a52be45c83a completed May 2, 2026, 9:11 p.m.
NEDg Description generation batch_69f6697e3a688190abd025df1112feba completed May 2, 2026, 9:15 p.m.
NED2 Entity disambiguation (via description) batch_69f66a9230608190bfe99290ca1679fa completed May 2, 2026, 9:20 p.m.
Created at: April 9, 2026, 5:20 p.m.