Triple

T7343153
Position Surface form Disambiguated ID Type / Status
Subject Sylwia E169307 entity
Predicate hasUsage P2529 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: [Sylwia, hasUsage, Poland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Poland
Context triple: [Sylwia, hasUsage, 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. 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.
  • E. Puolanka
    Puolanka is a small rural municipality in the Kainuu region of northern Finland, known for its scenic nature and self-deprecating “pessimism” tourism theme.
  • 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_69c68a57710481909f0c1f3c6ebdb6f2 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f0db2db8819088c4bed5d65571f6 completed March 27, 2026, 9:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7fa8a2a908190886e11a7d8df6c5e completed March 28, 2026, 3:58 p.m.
Created at: March 27, 2026, 3:04 p.m.