Triple

T8937987
Position Surface form Disambiguated ID Type / Status
Subject Berlin-Lichtenberg E212823 entity
Predicate hasTwinTown P919 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: [Berlin-Lichtenberg, hasTwinTown, Biesenthal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biesenthal
Context triple: [Berlin-Lichtenberg, hasTwinTown, 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. Osterburg
    Osterburg is a small town in the German state of Saxony-Anhalt, known for its historic architecture and rural surroundings.
  • C. Geisenfeld
    Geisenfeld is a small town in Bavaria, Germany, known as the birthplace of prominent early Nazi politician Gregor Strasser.
  • D. Haslach
    Haslach is a district or locality that forms part of the town of Oberkirch in the German state of Baden-Württemberg.
  • E. Haslach
    Haslach is a town in southern Germany historically noted as the site of the Battle of Haslach-Jungingen during the Napoleonic Wars.
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66b57a348190979effe4f9998eb7 completed April 1, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc937ded08190a67fd4457b6458ff completed April 3, 2026, 2:05 p.m.
Created at: March 30, 2026, 6:58 p.m.