Triple

T1486149
Position Surface form Disambiguated ID Type / Status
Subject Joseph Vilsmaier E29467 entity
Predicate spouse P13 FINISHED
Object Dana Vávrová E23595 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: Dana Vávrová | Statement: [Joseph Vilsmaier, spouse, Dana Vávrová]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dana Vávrová
Context triple: [Joseph Vilsmaier, spouse, Dana Vávrová]
  • A. Dana Vávrová chosen
    Dana Vávrová was a Czech-born German actress and film director known for her acclaimed performances in European cinema and collaborations with director Joseph Vilsmaier.
  • B. Milena Králíčková
    Milena Králíčková is a Czech academic and physician who serves as the rector of Charles University in Prague.
  • C. Hana Benešová
    Hana Benešová was the wife of Czechoslovak statesman and second president Edvard Beneš and served as the country's First Lady during his presidencies.
  • D. Terézia Mora
    Terézia Mora is a Hungarian-born German writer and translator acclaimed for her innovative prose and contributions to contemporary German-language literature.
  • E. Ivana Marie Zelníčková
    Ivana Marie Zelníčková, better known as Ivana Trump, was a Czech-American businesswoman, former model, and the first wife of Donald Trump, noted for her role in his early real estate empire and her own fashion and lifestyle ventures.
  • 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_69a498da82e08190ba833330d05f380f completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c6a1d8448190b3c90bb82fd806fe completed March 1, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad15b5aa348190bf6d7a3177eacaff completed March 8, 2026, 6:22 a.m.
Created at: March 1, 2026, 8:12 p.m.