Triple

T10690092
Position Surface form Disambiguated ID Type / Status
Subject Leszek Miller E251986 entity
Predicate replacedBy P101 FINISHED
Object Marek Belka E235712 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: Marek Belka | Statement: [Leszek Miller, replacedBy, Marek Belka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marek Belka
Context triple: [Leszek Miller, replacedBy, Marek Belka]
  • A. Marek Belka chosen
    Marek Belka is a Polish economist and politician who served as Prime Minister of Poland and later as president of the National Bank of Poland.
  • B. Marek Chodor
    Marek Chodor is an architect known for designing the Bełżec memorial and museum commemorating victims of the Holocaust in Poland.
  • C. Marek Zaleski
    Marek Zaleski is a Polish literary critic and essayist known for his work on modern Polish literature and literary theory.
  • D. Marek Sikora
    Marek Sikora was a Czech film and theater actor and director known for his work in late 20th-century Czechoslovak cinema and stage productions.
  • E. Marek Janowski
    Marek Janowski is a renowned Polish-born German conductor particularly celebrated for his interpretations of the German Romantic and Wagnerian operatic repertoire.
  • 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_69d6aa5bd7c08190a816e733b4045c23 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fd1c0f0081908a6869ee756ec789 completed April 9, 2026, 1:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69d988a59d6c8190a0e170acfb3af6da completed April 10, 2026, 11:32 p.m.
Created at: April 8, 2026, 9:11 p.m.