Triple

T6456521
Position Surface form Disambiguated ID Type / Status
Subject Friedrich Gottlieb Klopstock E142005 entity
Predicate placeOfBirth P1 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: [Friedrich Gottlieb Klopstock, placeOfBirth, Quedlinburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Quedlinburg
Context triple: [Friedrich Gottlieb Klopstock, placeOfBirth, 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_69c008d2f91c8190a8178767a35e08fc completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c069d639ec8190bb0a806da4118440 completed March 22, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c64bdc4e808190a7c24b963ab0aa30 completed March 27, 2026, 9:20 a.m.
Created at: March 22, 2026, 4:48 p.m.