Triple

T6748610
Position Surface form Disambiguated ID Type / Status
Subject Gauleiter of Berlin E154284 entity
Predicate partOf P40 FINISHED
Object Gau Berlin E581971 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: Gau Berlin | Statement: [Gauleiter of Berlin, partOf, Gau Berlin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gau Berlin
Context triple: [Gauleiter of Berlin, partOf, Gau Berlin]
  • A. Bad Godesberg
    Bad Godesberg is a district in the city of Bonn, Germany, known for its affluent residential areas, former diplomatic missions, and scenic location along the Rhine River.
  • B. Spandau
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • C. Berliner
    Berliner is a German-origin surname most notably associated with Emile Berliner, the inventor of the gramophone and a pioneer in sound recording technology.
  • D. Nazi Berlin chosen
    Nazi Berlin was the capital of Germany under Adolf Hitler’s National Socialist regime, serving as the political and administrative center of the Third Reich during World War II.
  • E. Sachsenhausen
    Sachsenhausen is a historic and culturally vibrant district of Frankfurt am Main, known for its traditional apple wine taverns, museums, and picturesque old town streets.
  • 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_69c6880ef37881909268a5a7299b9293 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1d8bfa48190a7fc48102258ae17 completed March 27, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70b180f188190b380909c46fbce40 completed March 27, 2026, 10:56 p.m.
Created at: March 27, 2026, 2:11 p.m.