Triple

T2136263
Position Surface form Disambiguated ID Type / Status
Subject Brandenburg E46660 entity
Predicate hasCity P316 FINISHED
Object Brandenburg an der Havel E213860 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: Brandenburg an der Havel | Statement: [Brandenburg, hasCity, Brandenburg an der Havel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brandenburg an der Havel
Context triple: [Brandenburg, hasCity, Brandenburg an der Havel]
  • A. Brandenburg an der Havel chosen
    Brandenburg an der Havel is a historic town in eastern Germany, considered one of the cradles of the state of Brandenburg and known for its medieval architecture and waterways.
  • B. Oranienburg
    Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
  • C. Magdeburg
    Magdeburg is a historic city in central Germany, known for its medieval cathedral, role as a major trading and industrial center, and location on the Elbe River.
  • D. Schwerin
    Schwerin is a historic city in northern Germany known for its picturesque lakeside setting and landmark Schwerin Castle.
  • E. Neustrelitz
    Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
  • 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_69a88a174ab48190a5db20c132e5dccf completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbdc4ce8c81908d143d5451681e6a completed March 7, 2026, 5:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69af6533d4b88190ac722cd1ebfa3d8f completed March 10, 2026, 12:26 a.m.
Created at: March 4, 2026, 7:44 p.m.