Triple

T14295497
Position Surface form Disambiguated ID Type / Status
Subject northwestern Poland E354427 entity
Predicate containsCity P294 FINISHED
Object Kołobrzeg E180362 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: Kołobrzeg | Statement: [northwestern Poland, containsCity, Kołobrzeg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kołobrzeg
Context triple: [northwestern Poland, containsCity, Kołobrzeg]
  • A. Kołobrzeg chosen
    Kołobrzeg is a historic Polish port and spa city on the Baltic Sea, known for its beaches, seaside resorts, and role as a popular tourist destination.
  • B. Koszalin
    Koszalin is a city in northwestern Poland near the Baltic Sea, known as a regional cultural and economic center.
  • C. Elbląg
    Elbląg is a historic city in northern Poland known for its reconstructed Old Town, medieval heritage, and role as an important port and industrial center.
  • D. Szczecin
    Szczecin is a large Polish city and important maritime and industrial center in northwestern Poland, situated near the Baltic Sea and the German border.
  • E. Myślibórz
    Myślibórz is a small historic town in northwestern Poland known for its medieval architecture and picturesque lakes.
  • 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_69d8278e17088190b328c5a9d4be74ff completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de717b35ec81908968994e65737c66 completed April 14, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69fea59d27bc81908ace0b7db9f57215 completed May 9, 2026, 3:10 a.m.
Created at: April 10, 2026, 1:11 a.m.