Triple

T22389605
Position Surface form Disambiguated ID Type / Status
Subject My Love E553479 entity
Predicate producer P490 FINISHED
Object David Kreuger NE NERFINISHED

How this triple was built (2 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: David Kreuger | Statement: [My Love, producer, David Kreuger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David Kreuger
Context triple: [My Love, producer, David Kreuger]
  • A. David Kreuger chosen
    David Kreuger is a Swedish music producer and songwriter known for his work on late-1990s and early-2000s pop hits, particularly within the Cheiron Studios team.
  • B. John Kruger
    John Kruger is the tough, resourceful U.S. Marshal protagonist in the 1996 action film "Eraser," portrayed by Arnold Schwarzenegger.
  • C. Alan Durband
    Alan Durband was a British teacher, writer, and influential drama educator from Liverpool, known for his popular guides to Shakespeare and his impact on English education.
  • D. Lugosi
    Lugosi is a Hungarian surname most famously associated with actor Bela Lugosi, renowned for his iconic portrayal of Count Dracula in early horror cinema.
  • E. Douglas Eugene Franco
    Douglas Eugene Franco is the father of American actor Dave Franco and was part of the Franco family known for its multiple members in the entertainment industry.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e4cf87c8190a1ff474daec326b7 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15858c13c819098fe66a50ecea7d7 completed April 29, 2026, 1:01 a.m.
Created at: April 16, 2026, 8:45 p.m.