Triple

T21364871
Position Surface form Disambiguated ID Type / Status
Subject Saxe-Weissenfels E526881 entity
Predicate includedTerritory P285 FINISHED
Object Querfurt NE NERFINISHED

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: Querfurt | Statement: [Saxe-Weissenfels, includedTerritory, Querfurt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Querfurt
Context triple: [Saxe-Weissenfels, includedTerritory, Querfurt]
  • A. Querfurt chosen
    Querfurt is a small historic town in the German state of Saxony-Anhalt, known for its well-preserved medieval castle and old town.
  • B. Nordhausen
    Nordhausen is a historic town in central Germany known for its medieval architecture, former role as a key trading center, and association with the nearby Mittelbau-Dora concentration camp site.
  • C. Staßfurt
    Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
  • D. Helmstedt
    Helmstedt is a historic town in Lower Saxony, Germany, known for its medieval architecture and former university.
  • E. Rudolstadt
    Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51d8a308190b09113b3b3f9bc15 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b06e1ba081908a96928a65da5fa2 completed April 22, 2026, 11:26 a.m.
Created at: April 16, 2026, 5:08 p.m.