Triple

T15283187
Position Surface form Disambiguated ID Type / Status
Subject Braunschweig region E365321 entity
Predicate hasCentralCity P3940 FINISHED
Object Braunschweig E72622 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: Braunschweig | Statement: [Braunschweig region, hasCentralCity, Braunschweig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Braunschweig
Context triple: [Braunschweig region, hasCentralCity, Braunschweig]
  • A. Braunschweig chosen
    Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
  • B. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • C. Bremen
    Bremen is a small village in Fairfield County, Ohio, known for its historic charm and tight-knit rural community.
  • D. Bremen
    Bremen is a small city in western Georgia, United States, known as a regional hub along major transportation routes and as part of the Atlanta metropolitan area’s outer region.
  • E. Bremen
    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.
  • 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_69d85a103d9081908c1ea6c4c73ac8e3 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00e51f82081909f63d14b589d5587 completed April 15, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff4c2a938081909ccd9fe7c5021dc6 completed May 9, 2026, 3 p.m.
Created at: April 10, 2026, 3:15 a.m.