Triple

T14115818
Position Surface form Disambiguated ID Type / Status
Subject Peter Høj E339771 entity
Predicate name P16 FINISHED
Object Peter Høj E339771 NE FINISHED

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: Peter Høj | Statement: [Peter Høj, name, Peter Høj]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Høj
Context triple: [Peter Høj, name, Peter Høj]
  • A. Peter Høj chosen
    Peter Høj is an Australian academic and university leader who has served as vice-chancellor at several major universities, including the University of Adelaide.
  • B. Mikkel Svane
    Mikkel Svane is a Danish entrepreneur best known as the co-founder and longtime CEO of the customer service software company Zendesk.
  • C. Mikkel Bondesen
    Mikkel Bondesen is a television producer known for his executive production work on series such as "The Comedians."
  • D. Peter Aalbæk Jensen
    Peter Aalbæk Jensen is a Danish film producer and co-founder of the influential production company Zentropa, known for his collaborations with prominent directors such as Lars von Trier.
  • E. Martin Stig Andersen
    Martin Stig Andersen is a Danish composer and sound designer best known for his atmospheric, experimental audio work in video games and film, including the acclaimed indie game Limbo.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d81c6a95b481909e39111e0c1f31ee completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de6010a03c81909f5f160f8d1fa8fa completed April 14, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7e04184819081633f9cfc0ccab9 completed May 7, 2026, 8:36 p.m.
Created at: April 9, 2026, 10:22 p.m.