Triple

T20562298
Position Surface form Disambiguated ID Type / Status
Subject Ludwigsburg district E504872 entity
Predicate hasTown P847 FINISHED
Object Besigheim NE NERFINISHED

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: Besigheim | Statement: [Ludwigsburg district, hasTown, Besigheim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Besigheim
Context triple: [Ludwigsburg district, hasTown, Besigheim]
  • A. Besigheim chosen
    Besigheim is a historic wine-growing town in southwestern Germany, known for its well-preserved medieval old town and scenic location between the Enz and Neckar rivers.
  • B. Ottmarsheim
    Ottmarsheim is a commune in northeastern France’s Alsace region, known for its historic Romanesque church and location along the Rhine.
  • C. Bischheim
    Bischheim is a suburban commune in northeastern France, located near Strasbourg in the Bas-Rhin department of the Grand Est region.
  • D. Wiehl
    Wiehl is a river in North Rhine-Westphalia, Germany, that flows through the Bergisches Land region before joining the Agger.
  • E. Wiehl
    Wiehl is a small town in western Germany’s North Rhine-Westphalia region, known for its picturesque setting in the hilly Bergisches Land and its mix of rural charm and light industry.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69e0b4b6587c8190aee63dc7cff244ea completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a79f906c819081163de9649ccb17 completed April 20, 2026, 10:24 p.m.
Created at: April 16, 2026, 11:39 a.m.