Triple

T7091656
Position Surface form Disambiguated ID Type / Status
Subject Mayor of Fürth E165208 entity
Predicate appliesToJurisdiction P82 FINISHED
Object City of Fürth E213765 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 Fürth | Statement: [Mayor of Fürth, appliesToJurisdiction, City of Fürth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Fürth
Context triple: [Mayor of Fürth, appliesToJurisdiction, City of Fürth]
  • A. Stadt Fürth chosen
    Stadt Fürth is a Bavarian city in Germany known for its rich Franconian cultural traditions, historic architecture, and vibrant local festivals.
  • B. City of Essen
    The City of Essen is a major urban center in Germany’s Ruhr area, historically significant as a medieval ecclesiastical seat and later as an important industrial and coal-mining hub.
  • C. Forchheim
    Forchheim is a town in Upper Franconia, Bavaria, Germany, known for its historic old town and location along major regional rail and road routes.
  • D. Stadt Nürnberg
    Stadt Nürnberg is the municipal government of the German city of Nuremberg, responsible for local administration, public services, and urban infrastructure.
  • E. Schweinfurt
    Schweinfurt is a city in northern Bavaria, Germany, historically known for its ball bearing industry and as a strategic target during World War II.
  • 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_69c6887e8c10819091cee237560d32da completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e53132288190b6da361d9c7218ab completed March 27, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7948dd1048190ae0252e6b4614030 completed March 28, 2026, 8:42 a.m.
Created at: March 27, 2026, 2:41 p.m.