Triple

T10634470
Position Surface form Disambiguated ID Type / Status
Subject Bergkamen E250540 entity
Predicate hasSubdivision P747 FINISHED
Object Werne
Werne is a small town in North Rhine-Westphalia, Germany, known for its historic center and location along the Lippe River.
E881417 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: Werne | Statement: [Bergkamen, hasSubdivision, Werne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Werne
Context triple: [Bergkamen, hasSubdivision, Werne]
  • A. Lüdenscheid
    Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
  • B. Bocholt
    Bocholt is a medium-sized German city in the state of North Rhine-Westphalia, known for its industrial heritage and proximity to the Dutch border.
  • C. Bocholt
    Bocholt is a municipality in the Belgian province of Limburg, known for its rural character and local brewing tradition.
  • D. Schwelm
    Schwelm is a small town in North Rhine-Westphalia, Germany, known as the administrative seat of the Ennepe-Ruhr district.
  • E. Warendorf
    Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
  • 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: Werne
Triple: [Bergkamen, hasSubdivision, Werne]
Generated description
Werne is a small town in North Rhine-Westphalia, Germany, known for its historic center and location along the Lippe River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Werne
Target entity description: Werne is a small town in North Rhine-Westphalia, Germany, known for its historic center and location along the Lippe River.
  • A. Lüdenscheid
    Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
  • B. Bocholt
    Bocholt is a municipality in the Belgian province of Limburg, known for its rural character and local brewing tradition.
  • C. Bocholt
    Bocholt is a medium-sized German city in the state of North Rhine-Westphalia, known for its industrial heritage and proximity to the Dutch border.
  • D. Schwelm
    Schwelm is a small town in North Rhine-Westphalia, Germany, known as the administrative seat of the Ennepe-Ruhr district.
  • E. Warendorf
    Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
  • 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_69d6aa5993448190a493b790b8f85010 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfab47bc819086684edc1b6dce74 completed April 8, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69dbace2a5388190bb685d347dd8aa6c completed April 12, 2026, 2:32 p.m.
NEDg Description generation batch_69dbaeb211088190a9118c71918584e5 completed April 12, 2026, 2:39 p.m.
NED2 Entity disambiguation (via description) batch_69dbaf7c999c819097a8cdf5bd82f648 completed April 12, 2026, 2:43 p.m.
Created at: April 8, 2026, 9:03 p.m.