Triple

T9439512
Position Surface form Disambiguated ID Type / Status
Subject Lahn-Dill-Kreis E227605 entity
Predicate hasTown P847 FINISHED
Object Herborn E302146 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: Herborn | Statement: [Lahn-Dill-Kreis, hasTown, Herborn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Herborn
Context triple: [Lahn-Dill-Kreis, hasTown, Herborn]
  • A. Herborn chosen
    Herborn is a historic town in the German state of Hesse, known for its medieval old town and former academy that was an important center of Reformed Protestant scholarship.
  • B. Wernborn
    Wernborn is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
  • C. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • D. Bergheim
    Bergheim is a municipality in the Austrian state of Salzburg, located just north of the city of Salzburg and known for its suburban character and proximity to the regional capital.
  • E. Bergheim
    Bergheim is a town in western Germany situated along the Erft River, known for its historical center and proximity to the Cologne region.
  • 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_69ca843884488190ad6cbe0153088234 completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7ee1c8c48190a2ae8673eee07e9a completed April 1, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69d12cd9ae548190bf985c72196eb0bd completed April 4, 2026, 3:23 p.m.
Created at: March 30, 2026, 7:50 p.m.