Triple

T10306124
Position Surface form Disambiguated ID Type / Status
Subject Annette Kurschus E241765 entity
Predicate placeOfBirth P1 FINISHED
Object Rotenburg an der Fulda E521581 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: Rotenburg an der Fulda | Statement: [Annette Kurschus, placeOfBirth, Rotenburg an der Fulda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rotenburg an der Fulda
Context triple: [Annette Kurschus, placeOfBirth, Rotenburg an der Fulda]
  • A. Rotenburg an der Fulda chosen
    Rotenburg an der Fulda is a historic small town in northeastern Hesse, Germany, situated along the Fulda River and known for its well-preserved half-timbered architecture.
  • B. Hersfeld-Rotenburg
    Hersfeld-Rotenburg is a rural district in eastern Hesse, Germany, known for its historic towns, forests, and location along the Fulda River.
  • C. Mainburg
    Mainburg is a Bavarian town in southern Germany known for its hop-growing industry and role in the Hallertau beer region.
  • D. Rotenburg (Wümme)
    Rotenburg (Wümme) is a small town in Lower Saxony, Germany, known for its rural surroundings and role as a local administrative and service center.
  • E. Hofgeismar
    Hofgeismar is a small historic town in the German state of Hesse, known for its medieval architecture and picturesque setting.
  • 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_69d381ac38808190a8ca7457c85b625b completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d30a6c888190acdd0a645247736a completed April 7, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d75026fb0881908e4d16b3fde531c0 completed April 9, 2026, 7:07 a.m.
Created at: April 6, 2026, 11:46 a.m.