Triple

T9010336
Position Surface form Disambiguated ID Type / Status
Subject Landkreis Günzburg E215451 entity
Predicate contains P35 FINISHED
Object Deisenhausen
Deisenhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
 E823061 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: Deisenhausen | Statement: [Landkreis Günzburg, contains, Deisenhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Deisenhausen
Context triple: [Landkreis Günzburg, contains, Deisenhausen]
  • A. Balzhausen
    Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
  • B. Irschenhausen
    Irschenhausen is a small village in Bavaria, Germany, known in part as the place where German field marshal Erich von Manstein died.
  • C. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • D. Ochsenhausen
    Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
  • E. Merzhausen
    Merzhausen is a village-level district that forms one of the subdivisions of the town of Usingen in the Hochtaunus region of Hesse, 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: Deisenhausen
Triple: [Landkreis Günzburg, contains, Deisenhausen]
Generated description
Deisenhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Deisenhausen
Target entity description: Deisenhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
  • A. Balzhausen
    Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
  • B. Irschenhausen
    Irschenhausen is a small village in Bavaria, Germany, known in part as the place where German field marshal Erich von Manstein died.
  • C. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • D. Ochsenhausen
    Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
  • E. Merzhausen
    Merzhausen is a village-level district that forms one of the subdivisions of the town of Usingen in the Hochtaunus region of Hesse, 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_69ca83a2bf088190986ee7a8eb90407d completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc69c1571881908d0b144786b5ee1f completed April 1, 2026, 12:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1cc2ca7a081908f597e9a58920d6e completed April 5, 2026, 2:42 a.m.
NEDg Description generation batch_69d1ccb043008190a3af47b234520891 completed April 5, 2026, 2:45 a.m.
NED2 Entity disambiguation (via description) batch_69d1cd04694881909ab19cb4c11fbd20 completed April 5, 2026, 2:46 a.m.
Created at: March 30, 2026, 7:06 p.m.