Triple

T4562159
Position Surface form Disambiguated ID Type / Status
Subject Steinheim am Albuch E121816 entity
Predicate hasSubdivision P747 FINISHED
Object Söhnstetten
Söhnstetten is a village in the Heidenheim district of Baden-Württemberg, Germany, known as a part of the municipality of Steinheim am Albuch on the Swabian Jura.
 E481826 NE FINISHED

How this triple was built (4 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: Söhnstetten | Statement: [Steinheim am Albuch, hasSubdivision, Söhnstetten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Söhnstetten
Context triple: [Steinheim am Albuch, hasSubdivision, Söhnstetten]
  • A. Hettstadt
    Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
  • B. Stetten
    Stetten is a locality within the town of Lichtenfels in the German state of Bavaria.
  • C. Gernsbach
    Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
  • D. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • E. Diedenhofen
    Diedenhofen is the historical German name for the town of Thionville in northeastern France, near the border with Luxembourg and Germany.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Söhnstetten
Triple: [Steinheim am Albuch, hasSubdivision, Söhnstetten]
Generated description
Söhnstetten is a village in the Heidenheim district of Baden-Württemberg, Germany, known as a part of the municipality of Steinheim am Albuch on the Swabian Jura.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Söhnstetten
Target entity description: Söhnstetten is a village in the Heidenheim district of Baden-Württemberg, Germany, known as a part of the municipality of Steinheim am Albuch on the Swabian Jura.
  • A. Hettstadt
    Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
  • B. Stetten
    Stetten is a locality within the town of Lichtenfels in the German state of Bavaria.
  • C. Gernsbach
    Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
  • D. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • E. Diedenhofen
    Diedenhofen is the historical German name for the town of Thionville in northeastern France, near the border with Luxembourg and Germany.
  • F. None of above. chosen

Provenance (5 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_69bd463f156881908a99aca69c5721ac completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd582f21648190b49284eb61b618c9 completed March 20, 2026, 2:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69be777f287c81909f700e22163ccc24 completed March 21, 2026, 10:48 a.m.
NEDg Description generation batch_69be7b680ec08190957346de5c5acfa9 completed March 21, 2026, 11:05 a.m.
NED2 Entity disambiguation (via description) batch_69be7bbeda508190ae27ab12c6748ed7 completed March 21, 2026, 11:06 a.m.
Created at: March 20, 2026, 1:09 p.m.