Triple
T7506570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münsterland |
E177404
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
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.
|
E758699
|
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: Dülmen | Statement: [Münsterland, hasCity, Dülmen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dülmen Context triple: [Münsterland, hasCity, Dülmen]
-
A.
Bentheim
Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
-
B.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
C.
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.
-
D.
Schwelm
Schwelm is a small town in North Rhine-Westphalia, Germany, known as the administrative seat of the Ennepe-Ruhr district.
-
E.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
- 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: Dülmen Triple: [Münsterland, hasCity, Dülmen]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dülmen Target entity description: 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.
-
A.
Bentheim
Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
-
B.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
C.
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.
-
D.
Schwelm
Schwelm is a small town in North Rhine-Westphalia, Germany, known as the administrative seat of the Ennepe-Ruhr district.
-
E.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b76a288190bb3608a5e3bfa212 |
completed | March 27, 2026, 9:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6e2cd9cc8190accfefed5eaa0b07 |
completed | April 3, 2026, 7:37 a.m. |
| NEDg | Description generation | batch_69cf7041b6bc81909924d1382b756746 |
completed | April 3, 2026, 7:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf714f22c48190a192c6fc32debfd6 |
completed | April 3, 2026, 7:50 a.m. |
Created at: March 27, 2026, 3:45 p.m.