Triple

T7084792
Position Surface form Disambiguated ID Type / Status
Subject Maria Aurora von Königsmarck E165047 entity
Predicate deathPlace P21 FINISHED
Object Quedlinburg E95916 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: Quedlinburg | Statement: [Maria Aurora von Königsmarck, deathPlace, Quedlinburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Quedlinburg
Context triple: [Maria Aurora von Königsmarck, deathPlace, Quedlinburg]
  • A. Quedlinburg chosen
    Quedlinburg is a historic German town on the northern edge of the Harz mountains, renowned for its well-preserved medieval architecture and UNESCO World Heritage–listed old town.
  • B. Halberstadt
    Halberstadt is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and role as a former episcopal seat.
  • C. Wolfenbüttel
    Wolfenbüttel is a historic town in Lower Saxony, Germany, known for its Renaissance castle and rich cultural heritage.
  • D. Wernigerode
    Wernigerode is a picturesque German town in Saxony-Anhalt known for its colorful half-timbered houses, medieval castle, and location on the northern slopes of the Harz Mountains.
  • E. Helmstedt
    Helmstedt is a historic town in Lower Saxony, Germany, known for its medieval architecture and former university.
  • 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_69c6887d98408190912b9580666b0c1d completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e511535c819098f60de54930380f completed March 27, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7948094ec8190856870dfd59fc13a completed March 28, 2026, 8:42 a.m.
Created at: March 27, 2026, 2:40 p.m.