Triple

T7217531
Position Surface form Disambiguated ID Type / Status
Subject The Way E149572 entity
Predicate hasCastMember P2308 FINISHED
Object Yorick van Wageningen E322232 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: Yorick van Wageningen | Statement: [The Way, hasCastMember, Yorick van Wageningen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yorick van Wageningen
Context triple: [The Way, hasCastMember, Yorick van Wageningen]
  • A. Yorick van Wageningen chosen
    Yorick van Wageningen is a Dutch actor known internationally for his roles in films such as the 2011 adaptation of "The Girl with the Dragon Tattoo."
  • B. Theo Heemskerk
    Theo Heemskerk was a Dutch politician who served as Prime Minister of the Netherlands in the early 20th century.
  • C. Dirk Roosenburg
    Dirk Roosenburg was a prominent Dutch architect known for his early 20th-century modernist designs and as the grandfather of architect Rem Koolhaas.
  • D. Theo de Meester
    Theo de Meester was a Dutch liberal politician who served as Prime Minister of the Netherlands in the early 20th century.
  • E. Maarten ’t Hart
    Maarten ’t Hart is a Dutch writer and biologist known for his psychologically rich novels and essays, often drawing on his strict religious upbringing and love of classical music.
  • 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_69c687eca814819095abb52316b1af80 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e99170d88190b1aef326a7d81134 completed March 27, 2026, 8:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cbfb46388190992cc98039e71748 completed March 28, 2026, 12:39 p.m.
Created at: March 27, 2026, 2:53 p.m.