Triple

T16488571
Position Surface form Disambiguated ID Type / Status
Subject Ludwik Geyer E400511 entity
Predicate countryOfBusiness P36797 FINISHED
Object Poland E5029 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: Poland | Statement: [Ludwik Geyer, countryOfBusiness, Poland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Poland
Context triple: [Ludwik Geyer, countryOfBusiness, 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. Franuś
    Franuś is a Polish diminutive form of the male given name Franciszek, used as an affectionate or familiar nickname.
  • E. Poland and Czech Republic
    Poland and the Czech Republic are neighboring Central European countries known for their shared history, cultural ties, and extensive cross-border rail connections.
  • 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_69d883813098819084f5409539723b59 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e32e084f388190887dfb3f6928f506 completed April 18, 2026, 7:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00679ecf4c819096e7f698b81fe25a completed May 10, 2026, 11:10 a.m.
Created at: April 10, 2026, 5:13 a.m.