Triple

T9179565
Position Surface form Disambiguated ID Type / Status
Subject Bottrop E220286 entity
Predicate hasDistrict P459 FINISHED
Object Vonderort
Vonderort is a district of the city of Bottrop in North Rhine-Westphalia, Germany.
 E781800 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: Vonderort | Statement: [Bottrop, hasDistrict, Vonderort]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vonderort
Context triple: [Bottrop, hasDistrict, Vonderort]
  • A. Ortenburg
    Ortenburg is a market town in Lower Bavaria, Germany, known for its historic castle and role as a former seat of the Counts of Ortenburg.
  • B. Niendorf
    Niendorf is a residential district in the northwestern part of Hamburg, Germany, known for its suburban character and proximity to Hamburg Airport.
  • C. Endenich
    Endenich is a district of Bonn, Germany, historically known as the place where composer Robert Schumann spent his final years and died in a mental asylum.
  • D. Todenfeld
    Todenfeld is a village and district of the town of Rheinbach in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
  • E. Neudorf
    Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
  • 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: Vonderort
Triple: [Bottrop, hasDistrict, Vonderort]
Generated description
Vonderort is a district of the city of Bottrop in North Rhine-Westphalia, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vonderort
Target entity description: Vonderort is a district of the city of Bottrop in North Rhine-Westphalia, Germany.
  • A. Ortenburg
    Ortenburg is a market town in Lower Bavaria, Germany, known for its historic castle and role as a former seat of the Counts of Ortenburg.
  • B. Niendorf
    Niendorf is a residential district in the northwestern part of Hamburg, Germany, known for its suburban character and proximity to Hamburg Airport.
  • C. Endenich
    Endenich is a district of Bonn, Germany, historically known as the place where composer Robert Schumann spent his final years and died in a mental asylum.
  • D. Todenfeld
    Todenfeld is a village and district of the town of Rheinbach in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
  • E. Neudorf
    Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
  • 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_69ca83e589948190ac9907819db11ddf completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccc25064588190856c96b229d9cd60 completed April 1, 2026, 6:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69d054b5512c8190aa7909ef8b56b186 completed April 4, 2026, midnight
NEDg Description generation batch_69d055c6b7f08190ad6ff81adffaeab1 completed April 4, 2026, 12:05 a.m.
NED2 Entity disambiguation (via description) batch_69d05655a27c8190b0445476c10f57ce completed April 4, 2026, 12:07 a.m.
Created at: March 30, 2026, 7:23 p.m.