Triple
T1972107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Josef Albers |
E42823
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Bottrop
Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
|
E220286
|
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: Bottrop | Statement: [Josef Albers, placeOfBirth, Bottrop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bottrop Context triple: [Josef Albers, placeOfBirth, Bottrop]
-
A.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
B.
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.
-
C.
Wuppertal
Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
-
D.
Handforth
Handforth is a village and civil parish in Cheshire, England, situated near the town of Wilmslow and forming part of the Greater Manchester commuter belt.
-
E.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
- 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: Bottrop Triple: [Josef Albers, placeOfBirth, Bottrop]
Generated description
Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bottrop Target entity description: Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
-
A.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
B.
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.
-
C.
Wuppertal
Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
-
D.
Handforth
Handforth is a village and civil parish in Cheshire, England, situated near the town of Wilmslow and forming part of the Greater Manchester commuter belt.
-
E.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
- 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_69a8871289048190b00b0d7744b7b2b1 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb3f275408190affa93f8cb6a8184 |
completed | March 7, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adfbdb5d7c8190ab3e7c368041c641 |
completed | March 8, 2026, 10:44 p.m. |
| NEDg | Description generation | batch_69adfcae8a348190aa5688d0d183c323 |
completed | March 8, 2026, 10:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adfd713c4081908a2fae2fada76dae |
completed | March 8, 2026, 10:51 p.m. |
Created at: March 4, 2026, 7:36 p.m.