Triple

T1624711
Position Surface form Disambiguated ID Type / Status
Subject District of Leipzig E35114 entity
Predicate contains P35 FINISHED
Object Wurzen
Wurzen is a historic town in the German state of Saxony, known for its medieval architecture and location on the river Mulde east of Leipzig.
 E289633 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: Wurzen | Statement: [District of Leipzig, contains, Wurzen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wurzen
Context triple: [District of Leipzig, contains, Wurzen]
  • A. Bautzen
    Bautzen is a historic town in eastern Germany known for its well-preserved medieval architecture and as a cultural center of the Sorbian minority.
  • B. Lankwitz
    Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
  • C. Zwickau
    Zwickau is a city in the German state of Saxony known historically as an important center of the automotive industry and as the birthplace of composer Robert Schumann.
  • D. Oranienburg
    Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
  • E. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • 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: Wurzen
Triple: [District of Leipzig, contains, Wurzen]
Generated description
Wurzen is a historic town in the German state of Saxony, known for its medieval architecture and location on the river Mulde east of Leipzig.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wurzen
Target entity description: Wurzen is a historic town in the German state of Saxony, known for its medieval architecture and location on the river Mulde east of Leipzig.
  • A. Bautzen
    Bautzen is a historic town in eastern Germany known for its well-preserved medieval architecture and as a cultural center of the Sorbian minority.
  • B. Lankwitz
    Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
  • C. Zwickau
    Zwickau is a city in the German state of Saxony known historically as an important center of the automotive industry and as the birthplace of composer Robert Schumann.
  • D. Oranienburg
    Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
  • E. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • 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_69a886023194819080a3fccd6e325d0e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909d0586c81909e399b636e130ff5 completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf15a4248190b898e3bfbeb2997a completed March 10, 2026, 5:41 a.m.
NEDg Description generation batch_69afb000ed448190a3d6db802eb88958 completed March 10, 2026, 5:45 a.m.
NED2 Entity disambiguation (via description) batch_69afb07e1d2c8190a8b8da3d3641b36c completed March 10, 2026, 5:47 a.m.
Created at: March 4, 2026, 7:28 p.m.