Triple

T10412902
Position Surface form Disambiguated ID Type / Status
Subject Luftfahrt-Bundesamt E245436 entity
Predicate headquartersLocation P62 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: [Luftfahrt-Bundesamt, headquartersLocation, Braunschweig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Braunschweig
Context triple: [Luftfahrt-Bundesamt, headquartersLocation, 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 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.
  • D. Bielefeld
    Bielefeld is a major city in northwestern Germany known for its industrial heritage, university, and the tongue-in-cheek “Bielefeld conspiracy” meme claiming it does not exist.
  • E. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • 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_69d381be340c8190b05998703d42d224 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea0e17f081908fb16425f65e5808 completed April 7, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69e343cd76448190b0583cc15005ac9d completed April 18, 2026, 8:41 a.m.
Created at: April 6, 2026, 12:10 p.m.