Triple

T20736426
Position Surface form Disambiguated ID Type / Status
Subject Leon Schiller E509720 entity
Predicate workLocation P7 FINISHED
Object Poland NE NERFINISHED

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: Poland | Statement: [Leon Schiller, workLocation, Poland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Poland
Context triple: [Leon Schiller, workLocation, Poland]
  • A. Poland chosen
    Poland is a Central European country known for its rich medieval heritage, resilient culture, and pivotal role in 20th-century history, including being the site of the outbreak of World War II.
  • B. Polonia
    Polonia refers to the global community of people of Polish origin living outside Poland, encompassing their cultural, social, and political organizations worldwide.
  • C. Polón
    Polón is a Finnish surname most notably associated with Eduard Polón, an industrialist and co-founder of the company that became part of Nokia.
  • D. Wasicsko
    Wasicsko is a surname most notably associated with Nick Wasicsko, a former mayor of Yonkers, New York, known for his role in the city’s public housing desegregation battle.
  • E. Franuś
    Franuś is a Polish diminutive form of the male given name Franciszek, used as an affectionate or familiar nickname.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c589c08190834fb5d86d0efa2b completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c20a02d48190bba22d1bdbeb370d completed April 21, 2026, 12:17 a.m.
Created at: April 16, 2026, 12:31 p.m.