Triple

T1624707
Position Surface form Disambiguated ID Type / Status
Subject District of Leipzig E35114 entity
Predicate capital P234 FINISHED
Object Borna
Borna is a town in the German state of Saxony that serves as an administrative and economic center in the Leipzig region.
 E185203 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: Borna | Statement: [District of Leipzig, capital, Borna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Borna
Context triple: [District of Leipzig, capital, Borna]
  • A. Čukarica
    Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
  • B. Barajevo
    Barajevo is a suburban municipality of Belgrade, Serbia, located in the southern part of the city’s administrative area.
  • C. Obrenovac
    Obrenovac is a suburban municipality of Belgrade in Serbia, known for its industrial facilities and proximity to the Sava River.
  • D. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • E. Lazarevac
    Lazarevac is a suburban municipality of Belgrade in central Serbia, known for its coal mining industry and the Kolubara coal basin.
  • 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: Borna
Triple: [District of Leipzig, capital, Borna]
Generated description
Borna is a town in the German state of Saxony that serves as an administrative and economic center in the Leipzig region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Borna
Target entity description: Borna is a town in the German state of Saxony that serves as an administrative and economic center in the Leipzig region.
  • A. Čukarica
    Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
  • B. Barajevo
    Barajevo is a suburban municipality of Belgrade, Serbia, located in the southern part of the city’s administrative area.
  • C. Obrenovac
    Obrenovac is a suburban municipality of Belgrade in Serbia, known for its industrial facilities and proximity to the Sava River.
  • D. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • E. Lazarevac
    Lazarevac is a suburban municipality of Belgrade in central Serbia, known for its coal mining industry and the Kolubara coal basin.
  • 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_69ad58ccc80c819088ecd91f0a99a247 completed March 8, 2026, 11:09 a.m.
NEDg Description generation batch_69ad5a619da481908d66837ea94c91cf completed March 8, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69ad5b41a68c8190ba293d8e8c35521b completed March 8, 2026, 11:19 a.m.
Created at: March 4, 2026, 7:28 p.m.