Triple

T8600798
Position Surface form Disambiguated ID Type / Status
Subject Landkreis Biberach E203668 entity
Predicate hasTown P847 FINISHED
Object Laupheim E775579 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: Laupheim | Statement: [Landkreis Biberach, hasTown, Laupheim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laupheim
Context triple: [Landkreis Biberach, hasTown, Laupheim]
  • A. Laupheim chosen
    Laupheim is a town in the district of Biberach in the German state of Baden-Württemberg, known historically for its Jewish community and as an industrial and cultural center in Upper Swabia.
  • B. Blaubeuren
    Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
  • C. Schwanau
    Schwanau is a municipality in southwestern Germany’s Baden-Württemberg region, situated near the Rhine River and the French border.
  • D. Heidenheim an der Brenz
    Heidenheim an der Brenz is a town in the German state of Baden-Württemberg known for its industrial heritage, historic castle Hellenstein, and location on the Brenz River near the Swabian Jura.
  • E. Schweinheim
    Schweinheim is a residential district within the Bad Godesberg borough of Bonn in western Germany.
  • 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_69ca832b56948190ba751cec255308f1 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc46d8ff408190acc7cd8dc99b2689 completed March 31, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d065ac77c08190af1c13cce87e0991 completed April 4, 2026, 1:13 a.m.
Created at: March 30, 2026, 6:24 p.m.