Triple
T9179569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bottrop |
E220286
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Dorsten
Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
|
E848517
|
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: Dorsten | Statement: [Bottrop, borderedBy, Dorsten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dorsten Context triple: [Bottrop, borderedBy, Dorsten]
-
A.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
B.
Datteln
Datteln is a town in North Rhine-Westphalia, Germany, known for its canal junction and industrial heritage.
-
C.
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.
-
D.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
E.
Warendorf
Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
- 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: Dorsten Triple: [Bottrop, borderedBy, Dorsten]
Generated description
Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dorsten Target entity description: Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
-
A.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
B.
Datteln
Datteln is a town in North Rhine-Westphalia, Germany, known for its canal junction and industrial heritage.
-
C.
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.
-
D.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
E.
Warendorf
Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
- 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_69d369226a28819088b14cbc4cb78e57 |
completed | April 6, 2026, 8:04 a.m. |
| NEDg | Description generation | batch_69d36a006da48190b98b325b7dc24caa |
completed | April 6, 2026, 8:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d36a5343588190923c3fe62368cd0a |
completed | April 6, 2026, 8:09 a.m. |
Created at: March 30, 2026, 7:23 p.m.