Triple

T9440826
Position Surface form Disambiguated ID Type / Status
Subject Augsburg district E227639 entity
Predicate contains P35 FINISHED
Object Oberhausen (Swabia)
Oberhausen (Swabia) is a municipality in the Bavarian region of Swabia in southern Germany.
 E798947 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: Oberhausen (Swabia) | Statement: [Augsburg district, contains, Oberhausen (Swabia)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Oberhausen (Swabia)
Context triple: [Augsburg district, contains, Oberhausen (Swabia)]
  • A. Oberhausen
    Oberhausen is an industrial city in Germany’s Ruhr region, historically known for its coal and steel production and heavily affected by World War II bombing.
  • B. Fritzlar
    Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
  • C. Gescher
    Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
  • D. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • E. Waldbröl
    Waldbröl is a small town in North Rhine-Westphalia, Germany, known for its rural setting in the Bergisches Land region.
  • 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: Oberhausen (Swabia)
Triple: [Augsburg district, contains, Oberhausen (Swabia)]
Generated description
Oberhausen (Swabia) is a municipality in the Bavarian region of Swabia in southern Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Oberhausen (Swabia)
Target entity description: Oberhausen (Swabia) is a municipality in the Bavarian region of Swabia in southern Germany.
  • A. Oberhausen
    Oberhausen is an industrial city in Germany’s Ruhr region, historically known for its coal and steel production and heavily affected by World War II bombing.
  • B. Fritzlar
    Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
  • C. Gescher
    Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
  • D. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • E. Waldbröl
    Waldbröl is a small town in North Rhine-Westphalia, Germany, known for its rural setting in the Bergisches Land region.
  • 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_69ca843884488190ad6cbe0153088234 completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7ee4f4a08190ada5ee14fec2b822 completed April 1, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1105dc6b48190bd6c7d932d9f48d5 completed April 4, 2026, 1:21 p.m.
NEDg Description generation batch_69d1112e140c8190ad6d9b3a25d48af7 completed April 4, 2026, 1:25 p.m.
NED2 Entity disambiguation (via description) batch_69d111b6c1d881909eb555a6a524c6db completed April 4, 2026, 1:27 p.m.
Created at: March 30, 2026, 7:50 p.m.