Triple

T10806019
Position Surface form Disambiguated ID Type / Status
Subject Walsum E254966 entity
Predicate hasPart P35 FINISHED
Object Vierlinden
Vierlinden is a district within the Walsum area of Duisburg in North Rhine-Westphalia, Germany.
E890836 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: Vierlinden | Statement: [Walsum, hasPart, Vierlinden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vierlinden
Context triple: [Walsum, hasPart, Vierlinden]
  • A. Lembeek
    Lembeek is a village in the Belgian municipality of Halle, located along the Senne River in the province of Flemish Brabant.
  • B. Zonhoven
    Zonhoven is a municipality in the Belgian province of Limburg, known for its green surroundings and proximity to the city of Hasselt.
  • C. Tessenderlo
    Tessenderlo is a municipality in the Belgian province of Limburg, known for its chemical industry and location near the Albert Canal.
  • D. Rupelmonde
    Rupelmonde is a village in Belgium best known as the birthplace of the renowned cartographer Gerardus Mercator.
  • E. Diepenbeek
    Diepenbeek is a municipality in the Belgian province of Limburg, known for its blend of residential areas, industry, and the campus of Hasselt University.
  • 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: Vierlinden
Triple: [Walsum, hasPart, Vierlinden]
Generated description
Vierlinden is a district within the Walsum area of Duisburg in North Rhine-Westphalia, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Vierlinden
Target entity description: Vierlinden is a district within the Walsum area of Duisburg in North Rhine-Westphalia, Germany.
  • A. Lembeek
    Lembeek is a village in the Belgian municipality of Halle, located along the Senne River in the province of Flemish Brabant.
  • B. Zonhoven
    Zonhoven is a municipality in the Belgian province of Limburg, known for its green surroundings and proximity to the city of Hasselt.
  • C. Tessenderlo
    Tessenderlo is a municipality in the Belgian province of Limburg, known for its chemical industry and location near the Albert Canal.
  • D. Rupelmonde
    Rupelmonde is a village in Belgium best known as the birthplace of the renowned cartographer Gerardus Mercator.
  • E. Diepenbeek
    Diepenbeek is a municipality in the Belgian province of Limburg, known for its blend of residential areas, industry, and the campus of Hasselt University.
  • 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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d733b3f92c8190bcc85db22d77bb7d completed April 9, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69dff7c0907c8190b092bb6754fe4e52 completed April 15, 2026, 8:40 p.m.
NEDg Description generation batch_69e0026e7900819087327db5f625169c completed April 15, 2026, 9:26 p.m.
NED2 Entity disambiguation (via description) batch_69e0057a7704819096becb74dc261883 completed April 15, 2026, 9:39 p.m.
Created at: April 8, 2026, 9:18 p.m.