Triple

T20849503
Position Surface form Disambiguated ID Type / Status
Subject B 3 E513317 entity
Predicate connects P390 FINISHED
Object Marburg NE NERFINISHED

How this triple was built (3 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: Marburg | Statement: [B 3, connects, Marburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marburg
Context triple: [B 3, connects, Marburg]
  • A. Marburg
    Marburg is a historic university town in central Germany known for its well-preserved medieval old town and the Philipps-Universität, one of the oldest Protestant universities in the world.
  • B. Marburg
    Marburg is a small rural township in Queensland, Australia, known for its historic buildings and location between Ipswich and Toowoomba.
  • C. Diemelstadt
    Diemelstadt is a small town in the German state of North Rhine-Westphalia, known for its rural character and location near the Diemel River.
  • D. Vienenburg
    Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
  • E. Riemst
    Riemst is a municipality in the Belgian province of Limburg, known for its rural character and location near the borders with the Netherlands and Germany.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Marburg
Target entity description: Marburg is a historic university town in the German state of Hesse, known for its well-preserved medieval old town and the Philipps-Universität Marburg.
  • A. Marburg chosen
    Marburg is a historic university town in central Germany known for its well-preserved medieval old town and the Philipps-Universität, one of the oldest Protestant universities in the world.
  • B. Marburg
    Marburg is a small rural township in Queensland, Australia, known for its historic buildings and location between Ipswich and Toowoomba.
  • C. Diemelstadt
    Diemelstadt is a small town in the German state of North Rhine-Westphalia, known for its rural character and location near the Diemel River.
  • D. Vienenburg
    Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
  • E. Riemst
    Riemst is a municipality in the Belgian province of Limburg, known for its rural character and location near the borders with the Netherlands and Germany.
  • F. None of above.

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_69e0b4f4898081908209e58edb8f9c45 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c3520b0081908ce0f43e8f20b24c completed April 21, 2026, 12:22 a.m.
Created at: April 16, 2026, 12:43 p.m.