Triple
T10806026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walsum |
E254966
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Voerde
Voerde is a town in the Wesel district of North Rhine-Westphalia, Germany, situated on the Lower Rhine in the Ruhr region.
|
E886732
|
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: Voerde | Statement: [Walsum, borderedBy, Voerde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Voerde Context triple: [Walsum, borderedBy, Voerde]
-
A.
Werl
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
-
B.
Soest
Soest is a Dutch town and municipality in the central Netherlands known for its green surroundings and proximity to the Utrechtse Heuvelrug.
-
C.
Soest
Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
-
D.
Venray
Venray is a town and municipality in the Dutch province of Limburg, known for its historic center and role in World War II.
-
E.
Dülmen
Dülmen is a town in western Germany’s North Rhine-Westphalia, known for its location between Münster and the Ruhr area and for the wild Dülmen ponies in the nearby nature reserve.
- 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: Voerde Triple: [Walsum, borderedBy, Voerde]
Generated description
Voerde is a town in the Wesel district of North Rhine-Westphalia, Germany, situated on the Lower Rhine in the Ruhr region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Voerde Target entity description: Voerde is a town in the Wesel district of North Rhine-Westphalia, Germany, situated on the Lower Rhine in the Ruhr region.
-
A.
Werl
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
-
B.
Soest
Soest is a Dutch town and municipality in the central Netherlands known for its green surroundings and proximity to the Utrechtse Heuvelrug.
-
C.
Soest
Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
-
D.
Venray
Venray is a town and municipality in the Dutch province of Limburg, known for its historic center and role in World War II.
-
E.
Dülmen
Dülmen is a town in western Germany’s North Rhine-Westphalia, known for its location between Münster and the Ruhr area and for the wild Dülmen ponies in the nearby nature reserve.
- 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_69de5680566c8190bd4ce7a736dc0e46 |
completed | April 14, 2026, 3 p.m. |
| NEDg | Description generation | batch_69de5eaf3cc08190935cb6ddf2020166 |
completed | April 14, 2026, 3:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de63a902f4819089845bc6d7469c6b |
completed | April 14, 2026, 3:56 p.m. |
Created at: April 8, 2026, 9:18 p.m.