Triple

T4553022
Position Surface form Disambiguated ID Type / Status
Subject Zelníčková E120412 entity
Predicate notableBearer P458 FINISHED
Object Ivana Marie Zelníčková E21879 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: Ivana Marie Zelníčková | Statement: [Zelníčková, notableBearer, Ivana Marie Zelníčková]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ivana Marie Zelníčková
Context triple: [Zelníčková, notableBearer, Ivana Marie Zelníčková]
  • A. Ivana Marie Zelníčková chosen
    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.
  • B. Livia Klausová
    Livia Klausová is a Slovak-born Czech economist and diplomat who served as the First Lady of the Czech Republic and later as Czech ambassador to Slovakia.
  • C. Jana Fialová
    Jana Fialová is known primarily as the wife of Czech politician and Prime Minister Petr Fiala.
  • D. Markéta Vaňková
    Markéta Vaňková is a Czech politician who serves as the mayor of Brno, one of the country’s largest cities.
  • E. Milena Králíčková
    Milena Králíčková is a Czech academic and physician who serves as the rector of Charles University in Prague.
  • 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_69bd4636f1648190a701445c2fcd9c17 completed March 20, 2026, 1:05 p.m.
NER Named-entity recognition batch_69bd581160e08190b715a8ce5c3e6c9b completed March 20, 2026, 2:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdb95b01b0819094a600752e41aa09 completed March 20, 2026, 9:17 p.m.
Created at: March 20, 2026, 1:09 p.m.