Triple

T9540666
Position Surface form Disambiguated ID Type / Status
Subject Regen (district) E230146 entity
Predicate contains P35 FINISHED
Object Kollnburg
Kollnburg is a small municipality in the Bavarian Forest region of southeastern Germany, known for its rural landscape and historic castle ruins.
 E817296 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: Kollnburg | Statement: [Regen (district), contains, Kollnburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kollnburg
Context triple: [Regen (district), contains, Kollnburg]
  • A. Kuppenheim
    Kuppenheim is a small town in the Rastatt district of Baden-Württemberg, southwestern Germany, situated near the Black Forest.
  • B. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • C. Günsberg
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • D. Burgstädt
    Burgstädt is a small town in the German state of Saxony, known for its traditional architecture and location near the city of Chemnitz.
  • E. Marlenheim
    Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
  • 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: Kollnburg
Triple: [Regen (district), contains, Kollnburg]
Generated description
Kollnburg is a small municipality in the Bavarian Forest region of southeastern Germany, known for its rural landscape and historic castle ruins.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kollnburg
Target entity description: Kollnburg is a small municipality in the Bavarian Forest region of southeastern Germany, known for its rural landscape and historic castle ruins.
  • A. Kuppenheim
    Kuppenheim is a small town in the Rastatt district of Baden-Württemberg, southwestern Germany, situated near the Black Forest.
  • B. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • C. Günsberg
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • D. Burgstädt
    Burgstädt is a small town in the German state of Saxony, known for its traditional architecture and location near the city of Chemnitz.
  • E. Marlenheim
    Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
  • 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_69ca847b1b3081908f72bc932c17cc41 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98e695948190ab107fff38c57de7 completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19f60aa508190b3966f4b917c41b5 completed April 4, 2026, 11:31 p.m.
NEDg Description generation batch_69d1a3cc5420819091ee338da5afe4b7 completed April 4, 2026, 11:50 p.m.
NED2 Entity disambiguation (via description) batch_69d1a5f265148190af432e3640221a33 completed April 4, 2026, 11:59 p.m.
Created at: March 30, 2026, 8:01 p.m.