Triple

T3066690
Position Surface form Disambiguated ID Type / Status
Subject Quills E62118 entity
Predicate cinematographyBy P1953 FINISHED
Object Rogier Stoffers E162439 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: Rogier Stoffers | Statement: [Quills, cinematographyBy, Rogier Stoffers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rogier Stoffers
Context triple: [Quills, cinematographyBy, Rogier Stoffers]
  • A. Rogier Stoffers chosen
    Rogier Stoffers is a Dutch cinematographer known for his work on a range of international films and television productions.
  • B. Jan Verkolje
    Jan Verkolje was a 17th-century Dutch painter and mezzotint engraver known for his portraits and genre scenes in the Baroque style.
  • C. Johannes Uytenbogaert
    Johannes Uytenbogaert was a leading Dutch Remonstrant minister and theologian of the early 17th century, known as a chief spokesman for Arminianism in the Netherlands.
  • D. Bart van der Leck
    Bart van der Leck was a Dutch painter and designer associated with early abstraction, best known as a co-founder of the De Stijl movement alongside artists like Piet Mondrian.
  • E. Leo Geurts
    Leo Geurts was a Dutch computer scientist known for co-developing the ABC programming language, an influential precursor to Python.
  • 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada0fd87308190918e7b616f033faa completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20f45f6a08190a79df4c7a7846320 completed March 12, 2026, 12:56 a.m.
Created at: March 8, 2026, 3:02 p.m.