Triple

T6712794
Position Surface form Disambiguated ID Type / Status
Subject Kreuzberg E153187 entity
Predicate hasCityPart P12399 FINISHED
Object Bergmannkiez
Bergmannkiez is a popular, lively neighborhood in Berlin known for its historic architecture, café-lined streets, and vibrant cultural scene.
 E613474 NE FINISHED

How this triple was built (4 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: Bergmannkiez | Statement: [Kreuzberg, hasCityPart, Bergmannkiez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bergmannkiez
Context triple: [Kreuzberg, hasCityPart, Bergmannkiez]
  • A. Biesenthal
    Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
  • B. Marienfelde
    Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
  • C. Neubukow
    Neubukow is a small town in northern Germany best known as the birthplace of archaeologist Heinrich Schliemann.
  • D. Alsergrund
    Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to the city center.
  • E. Lippendorf
    Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bergmannkiez
Triple: [Kreuzberg, hasCityPart, Bergmannkiez]
Generated description
Bergmannkiez is a popular, lively neighborhood in Berlin known for its historic architecture, café-lined streets, and vibrant cultural scene.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bergmannkiez
Target entity description: Bergmannkiez is a popular, lively neighborhood in Berlin known for its historic architecture, café-lined streets, and vibrant cultural scene.
  • A. Biesenthal
    Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
  • B. Marienfelde
    Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
  • C. Neubukow
    Neubukow is a small town in northern Germany best known as the birthplace of archaeologist Heinrich Schliemann.
  • D. Alsergrund
    Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to the city center.
  • E. Lippendorf
    Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
  • F. None of above. chosen

Provenance (5 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_69c68809b4608190a2509ddb5ab87f05 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d121a92c8190a03f384a8aba84da completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c700948788819087f9b466be337286 completed March 27, 2026, 10:11 p.m.
NEDg Description generation batch_69c703ad7e0c81908da32c96806f3b07 completed March 27, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_69c7042f23408190b06faafcb3251276 completed March 27, 2026, 10:26 p.m.
Created at: March 27, 2026, 2:07 p.m.