Triple

T6630023
Position Surface form Disambiguated ID Type / Status
Subject Battle of Bautzen E149898 entity
Predicate near P350 FINISHED
Object Weissenberg
Weissenberg is a small town in eastern Saxony, Germany, known historically for its proximity to the Napoleonic-era Battle of Bautzen.
 E600983 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: Weissenberg | Statement: [Battle of Bautzen, near, Weissenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Weissenberg
Context triple: [Battle of Bautzen, near, Weissenberg]
  • A. Weisselberg
    Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
  • B. Löwenberg
    Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
  • C. Nischel
    Nischel is the local colloquial nickname for the large Karl Marx Monument in Chemnitz, Germany.
  • D. Schlieren
    Schlieren is a municipality in the canton of Zurich in northern Switzerland, known as a suburban town within the Zurich metropolitan area.
  • E. Leissigen
    Leissigen is a Swiss village in the canton of Bern, known for its scenic location in the Bernese Oberland on the shores of Lake Thun.
  • 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: Weissenberg
Triple: [Battle of Bautzen, near, Weissenberg]
Generated description
Weissenberg is a small town in eastern Saxony, Germany, known historically for its proximity to the Napoleonic-era Battle of Bautzen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Weissenberg
Target entity description: Weissenberg is a small town in eastern Saxony, Germany, known historically for its proximity to the Napoleonic-era Battle of Bautzen.
  • A. Weisselberg
    Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
  • B. Löwenberg
    Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
  • C. Nischel
    Nischel is the local colloquial nickname for the large Karl Marx Monument in Chemnitz, Germany.
  • D. Schlieren
    Schlieren is a municipality in the canton of Zurich in northern Switzerland, known as a suburban town within the Zurich metropolitan area.
  • E. Leissigen
    Leissigen is a Swiss village in the canton of Bern, known for its scenic location in the Bernese Oberland on the shores of Lake Thun.
  • 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_69c687ee50048190aa151765bef16193 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6afa5c9b48190b645be96d446d0ca completed March 27, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cbeb04348190957b8e5f098b72bf 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_69c6cdcf14508190876faa73f5eec884 completed March 27, 2026, 6:34 p.m.
Created at: March 27, 2026, 1:59 p.m.