Triple

T2825983
Position Surface form Disambiguated ID Type / Status
Subject Setagaya E54921 entity
Predicate hasSisterCity P919 FINISHED
Object Bremen, Germany E76455 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: Bremen, Germany | Statement: [Setagaya, hasSisterCity, Bremen, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bremen, Germany
Context triple: [Setagaya, hasSisterCity, Bremen, Germany]
  • A. Brunswick, Germany
    Brunswick, Germany is a historic city in Lower Saxony known for its medieval architecture, former status as a ducal residence, and role as an important commercial and cultural center in northern Germany.
  • B. Bremen chosen
    Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
  • C. Hamm, Germany
    Hamm is a city in the German state of North Rhine-Westphalia, known as an industrial and transportation hub in the eastern Ruhr area.
  • D. Flensburg, Germany
    Flensburg, Germany is a historic port city in northern Germany near the Danish border, known for its maritime heritage and role as the last seat of the German government at the end of World War II.
  • E. Hamburg-Finkenwerder, Germany
    Hamburg-Finkenwerder, Germany is an industrial district of Hamburg best known for its large Airbus manufacturing and assembly facilities.
  • 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_69ab49e100c0819082a40cb797383243 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde94ba848190b2c990936e07e6bb completed March 7, 2026, 8:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69b055d3dbb8819094df5e6751dd96c4 completed March 10, 2026, 5:33 p.m.
Created at: March 6, 2026, 9:59 p.m.