Triple

T4636977
Position Surface form Disambiguated ID Type / Status
Subject Reichsgau Danzig-West Prussia E101554 entity
Predicate notableCity P2813 FINISHED
Object Bydgoszcz E390387 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: Bydgoszcz | Statement: [Reichsgau Danzig-West Prussia, notableCity, Bydgoszcz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bydgoszcz
Context triple: [Reichsgau Danzig-West Prussia, notableCity, Bydgoszcz]
  • A. Bydgoszcz chosen
    Bydgoszcz is a major city in northern Poland known as an important economic, cultural, and academic center on the Brda and Vistula rivers.
  • B. Gdańsk
    Gdańsk is a major Polish port city on the Baltic Sea, known for its rich Hanseatic history, shipyards, and role in the origins of the Solidarity movement.
  • C. 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.
  • D. Olsztyn
    Olsztyn is a historic city in northern Poland known for its medieval architecture, lakes, and role as the capital of the Warmian-Masurian Voivodeship.
  • E. Gdynia
    Gdynia is a major seaport city on Poland’s Baltic coast, developed rapidly in the 20th century into one of the country’s key maritime and economic centers.
  • 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_69bd43d2f1c081908cd4b7ec48ecc73d completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a62a9e48190b0cf1cbcc51f00c0 completed March 20, 2026, 2:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7bf5fcf588190bcd52c539c3958b0 completed March 28, 2026, 11:45 a.m.
Created at: March 20, 2026, 1:13 p.m.