Triple
T6551820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District of Borken |
E151146
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Stadtlohn
Stadtlohn is a small town in western Germany’s Münsterland region, near the Dutch border, known for its rural character and local industry.
|
E604599
|
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: Stadtlohn | Statement: [District of Borken, contains, Stadtlohn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stadtlohn Context triple: [District of Borken, contains, Stadtlohn]
-
A.
Langenau
Langenau is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its historic center and proximity to the Swabian Jura.
-
B.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
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.
Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
E.
Lungern
Lungern is a picturesque Swiss village in the canton of Obwalden, known for its lakeside setting amid alpine mountains and its popularity for outdoor recreation.
- 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: Stadtlohn Triple: [District of Borken, contains, Stadtlohn]
Generated description
Stadtlohn is a small town in western Germany’s Münsterland region, near the Dutch border, known for its rural character and local industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stadtlohn Target entity description: Stadtlohn is a small town in western Germany’s Münsterland region, near the Dutch border, known for its rural character and local industry.
-
A.
Langenau
Langenau is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its historic center and proximity to the Swabian Jura.
-
B.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
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.
Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
E.
Lungern
Lungern is a picturesque Swiss village in the canton of Obwalden, known for its lakeside setting amid alpine mountains and its popularity for outdoor recreation.
- 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_69c687f3fd60819083bfa583e5bcfa71 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ae05cd988190a013226b14cd98f0 |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d55416e48190b574e37a6f2e6690 |
completed | March 27, 2026, 7:07 p.m. |
| NEDg | Description generation | batch_69c6d6acc2208190ac47c60bb896c1cd |
completed | March 27, 2026, 7:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6d83b53e48190881a3e1e8fa8b168 |
completed | March 27, 2026, 7:19 p.m. |
Created at: March 27, 2026, 1:51 p.m.