Triple

T3900166
Position Surface form Disambiguated ID Type / Status
Subject Langwasser depot E90467 entity
Predicate ownedBy P347 FINISHED
Object City of Nuremberg E408998 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: City of Nuremberg | Statement: [Langwasser depot, ownedBy, City of Nuremberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Nuremberg
Context triple: [Langwasser depot, ownedBy, City of Nuremberg]
  • A. Nuremberg
    Nuremberg is a historic city in Bavaria, Germany, known for its medieval architecture and its role as the site of the post–World War II war crimes tribunals.
  • B. Stadt Nürnberg chosen
    Stadt Nürnberg is the municipal government of the German city of Nuremberg, responsible for local administration, public services, and urban infrastructure.
  • C. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • D. Augsburg
    Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
  • E. Weimar
    Weimar is a historic German city renowned as a center of culture and the arts, associated with figures like Goethe and Schiller and pivotal movements in modern design and architecture.
  • 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_69aed95d315881908cbf1bf4a7215fbf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeecf17ef4819083db0a22e24d5b89 completed March 9, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b3d66ec819087616d434d594095 completed March 14, 2026, 2:05 p.m.
Created at: March 9, 2026, 3:21 p.m.