Triple
T12420564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leverkusen |
E296756
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Schlebusch
Schlebusch is a residential and commercial district of the German city of Leverkusen, known for its green spaces and local shopping streets.
|
E983032
|
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: Schlebusch | Statement: [Leverkusen, hasDistrict, Schlebusch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schlebusch Context triple: [Leverkusen, hasDistrict, Schlebusch]
-
A.
Dassow
Dassow is a small town in northern Germany’s Mecklenburg-Vorpommern region, near the Baltic Sea coast and the border with Schleswig-Holstein.
-
B.
Heringsdorf
Heringsdorf is a seaside resort town on the Baltic Sea coast of the island of Usedom in northeastern Germany, known for its historic pier and spa architecture.
-
C.
Wrangelsburg
Wrangelsburg is a historic estate and locality in northeastern Germany associated with the 17th-century Swedish field marshal and statesman Carl Gustaf Wrangel.
-
D.
Gadebusch
Gadebusch is a small historic town in northern Germany known for its medieval architecture and rural surroundings.
-
E.
Retzow
Retzow is a small municipality in the Havelland district of the federal state of Brandenburg in northeastern Germany.
- 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: Schlebusch Triple: [Leverkusen, hasDistrict, Schlebusch]
Generated description
Schlebusch is a residential and commercial district of the German city of Leverkusen, known for its green spaces and local shopping streets.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schlebusch Target entity description: Schlebusch is a residential and commercial district of the German city of Leverkusen, known for its green spaces and local shopping streets.
-
A.
Dassow
Dassow is a small town in northern Germany’s Mecklenburg-Vorpommern region, near the Baltic Sea coast and the border with Schleswig-Holstein.
-
B.
Heringsdorf
Heringsdorf is a seaside resort town on the Baltic Sea coast of the island of Usedom in northeastern Germany, known for its historic pier and spa architecture.
-
C.
Wrangelsburg
Wrangelsburg is a historic estate and locality in northeastern Germany associated with the 17th-century Swedish field marshal and statesman Carl Gustaf Wrangel.
-
D.
Gadebusch
Gadebusch is a small historic town in northern Germany known for its medieval architecture and rural surroundings.
-
E.
Retzow
Retzow is a small municipality in the Havelland district of the federal state of Brandenburg in northeastern Germany.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6efd748190a5d9396a343e41e1 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f0265fc81909a6288d11b78c2f9 |
completed | May 2, 2026, 6:14 p.m. |
| NEDg | Description generation | batch_69f6403711bc8190b214d4b06792a538 |
completed | May 2, 2026, 6:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f640f543c08190b95b16a8909eebf8 |
completed | May 2, 2026, 6:22 p.m. |
Created at: April 8, 2026, 9:55 p.m.