Triple

T17360537
Position Surface form Disambiguated ID Type / Status
Subject Basement Jaxx E422055 entity
Predicate notableWork P4 FINISHED
Object Rendez-Vu — NE ONDG

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: Rendez-Vu | Statement: [Basement Jaxx, notableWork, Rendez-Vu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rendez-Vu
Context triple: [Basement Jaxx, notableWork, Rendez-Vu]
  • A. Rendez-Vous
    Rendez-Vous is a 1986 electronic music album by French composer Jean-Michel Jarre, known for its lush synthesizer soundscapes and space-themed compositions.
  • B. Les Rendezvous
    Les Rendezvous is a light-hearted, plotless ballet choreographed by Frederick Ashton that showcases classical technique and ensemble dancing.
  • C. Le Rendez-Vous
    Le Rendez-Vous was the official slogan of UEFA Euro 2016, encapsulating the tournament’s theme of a shared meeting point for football fans across Europe.
  • D. The Rendezvous
    The Rendezvous is a 2016 adventure-romance film that follows an American doctor and a Jewish-American bureaucrat on a perilous journey in the Middle East after discovering a clue to a biblical mystery.
  • E. Rendezvous in Black
    Rendezvous in Black is a 1948 noir crime novel by Cornell Woolrich, centered on a man’s obsessive, methodical quest for revenge after a tragic accident.
  • 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: Rendez-Vu
Triple: [Basement Jaxx, notableWork, Rendez-Vu]
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rendez-Vu
Target entity description: "Rendez-Vu" is a 1999 dance track by British electronic music duo Basement Jaxx, known for its catchy hook and fusion of house, Latin, and UK club influences.
  • A. Rendez-Vous
    Rendez-Vous is a 1986 electronic music album by French composer Jean-Michel Jarre, known for its lush synthesizer soundscapes and space-themed compositions.
  • B. Les Rendezvous
    Les Rendezvous is a light-hearted, plotless ballet choreographed by Frederick Ashton that showcases classical technique and ensemble dancing.
  • C. Le Rendez-Vous
    Le Rendez-Vous was the official slogan of UEFA Euro 2016, encapsulating the tournament’s theme of a shared meeting point for football fans across Europe.
  • D. The Rendezvous
    The Rendezvous is a 2016 adventure-romance film that follows an American doctor and a Jewish-American bureaucrat on a perilous journey in the Middle East after discovering a clue to a biblical mystery.
  • E. Rendezvous in Black
    Rendezvous in Black is a 1948 noir crime novel by Cornell Woolrich, centered on a man’s obsessive, methodical quest for revenge after a tragic accident.
  • F. None of above. chosen

Provenance (4 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_69d889d520008190a26917a95bf1c2ea completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a4cacd881909fd722068b019f25 completed April 19, 2026, 2:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0195609d988190b7a70f8eabf75eaa completed May 11, 2026, 8:37 a.m.
NEDg Description generation batch_6a01962ae4848190b2aad8e19bf6522f in_progress May 11, 2026, 8:41 a.m.
Created at: April 10, 2026, 5:44 a.m.