Triple
T653416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wieliczka |
E11392
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Bergkamen
Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
|
E250540
|
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: Bergkamen | Statement: [Wieliczka, hasTwinTown, Bergkamen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bergkamen Context triple: [Wieliczka, hasTwinTown, Bergkamen]
-
A.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
-
B.
Andernach
Andernach is a historic German town on the Rhine River in Rhineland-Palatinate, known for its medieval architecture and one of the world’s highest cold-water geysers.
-
C.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
D.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
E.
Bottrop
Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
- 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: Bergkamen Triple: [Wieliczka, hasTwinTown, Bergkamen]
Generated description
Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bergkamen Target entity description: Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
-
A.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
-
B.
Andernach
Andernach is a historic German town on the Rhine River in Rhineland-Palatinate, known for its medieval architecture and one of the world’s highest cold-water geysers.
-
C.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
D.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
E.
Bottrop
Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4a660c8190b887cb4da01ef7ae |
completed | March 1, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7190fb84819095e6ebf2eeb50148 |
completed | March 9, 2026, 7:06 a.m. |
| NEDg | Description generation | batch_69ae75ad4f188190ba6edb78f0c32843 |
completed | March 9, 2026, 7:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae7610cb28819084c1898c4103aceb |
completed | March 9, 2026, 7:26 a.m. |
Created at: March 1, 2026, 7:36 p.m.