Triple

T15687353
Position Surface form Disambiguated ID Type / Status
Subject Barnim E380235 entity
Predicate hasMunicipality P847 FINISHED
Object Biesenthal E324318 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: Biesenthal | Statement: [Barnim, hasMunicipality, Biesenthal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biesenthal
Context triple: [Barnim, hasMunicipality, Biesenthal]
  • A. Biesenthal chosen
    Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
  • B. Lautenthal
    Lautenthal is a small historic mining town in Germany’s Harz Mountains, known for its picturesque valley setting and former silver mining industry.
  • C. Baar-Ebenhausen
    Baar-Ebenhausen is a Bavarian municipality in southern Germany known for its residential character and location along the Ilm River.
  • D. Bartenstein
    Bartenstein is the former German name of the town now known as Bartoszyce in northeastern Poland, historically part of East Prussia.
  • E. Niedenstein
    Niedenstein is a small town in central Germany known for its scenic location near the Habichtswald hills and its traditional half-timbered architecture.
  • 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_69d86d99e860819094b6957cde470f2c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04f4cee5481908699fbb2b7bdd2f6 completed April 16, 2026, 2:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff6ee91340819086c8f51e8eb477aa completed May 9, 2026, 5:29 p.m.
Created at: April 10, 2026, 4:44 a.m.