Triple

T6624524
Position Surface form Disambiguated ID Type / Status
Subject Schweinfurt region E149760 entity
Predicate hasRiver P165 FINISHED
Object Wern
The Wern is a river in northern Bavaria, Germany, that flows through the Schweinfurt region before joining the Main River.
 E599926 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: Wern | Statement: [Schweinfurt region, hasRiver, Wern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wern
Context triple: [Schweinfurt region, hasRiver, Wern]
  • A. Werneuchen
    Werneuchen is a small town in the German state of Brandenburg, located northeast of Berlin and characterized by its rural surroundings and commuter links to the capital.
  • B. Wurmberg
    Wurmberg is a prominent mountain in the Harz range of central Germany, popular for skiing, hiking, and panoramic views.
  • C. Wossek
    Wossek is a small town in what is now the Czech Republic, historically part of the Austro-Hungarian Empire and known as the birthplace of Hermann Kafka, father of writer Franz Kafka.
  • D. Wiehe
    Wiehe is a small town in the German state of Thuringia, historically notable as the birthplace of the influential 19th-century historian Leopold von Ranke.
  • E. Hellenstein
    Hellenstein is the historical namesake associated with Hellenstein Castle, a prominent medieval fortress in Heidenheim, 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: Wern
Triple: [Schweinfurt region, hasRiver, Wern]
Generated description
The Wern is a river in northern Bavaria, Germany, that flows through the Schweinfurt region before joining the Main River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wern
Target entity description: The Wern is a river in northern Bavaria, Germany, that flows through the Schweinfurt region before joining the Main River.
  • A. Werneuchen
    Werneuchen is a small town in the German state of Brandenburg, located northeast of Berlin and characterized by its rural surroundings and commuter links to the capital.
  • B. Wurmberg
    Wurmberg is a prominent mountain in the Harz range of central Germany, popular for skiing, hiking, and panoramic views.
  • C. Wossek
    Wossek is a small town in what is now the Czech Republic, historically part of the Austro-Hungarian Empire and known as the birthplace of Hermann Kafka, father of writer Franz Kafka.
  • D. Wiehe
    Wiehe is a small town in the German state of Thuringia, historically notable as the birthplace of the influential 19th-century historian Leopold von Ranke.
  • E. Hellenstein
    Hellenstein is the historical namesake associated with Hellenstein Castle, a prominent medieval fortress in Heidenheim, 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_69c687ed8a9c81908bb671717cb192ef completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af7fc054819099a2e58cefd8fed7 completed March 27, 2026, 4:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cbe690548190a771bb1ec8d3aacf completed March 27, 2026, 6:26 p.m.
NEDg Description generation batch_69c6cd0a98908190a5725c49bad7589d completed March 27, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69c6cdcc10c08190aa98212bd17063a3 completed March 27, 2026, 6:34 p.m.
Created at: March 27, 2026, 1:58 p.m.