Triple
T12580912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rüthen |
E300331
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Kellinghausen-Siedlung
Kellinghausen-Siedlung is a residential district within the town of Rüthen in North Rhine-Westphalia, Germany.
|
E991064
|
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: Kellinghausen-Siedlung | Statement: [Rüthen, hasSubdivision, Kellinghausen-Siedlung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kellinghausen-Siedlung Context triple: [Rüthen, hasSubdivision, Kellinghausen-Siedlung]
-
A.
Gartenstadt
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
-
B.
Niederschönhausen
Niederschönhausen is a residential district in the Berlin borough of Pankow, known for its historic villas, green spaces, and the former presidential residence Schloss Schönhausen.
-
C.
Wilhelmsdorf
Wilhelmsdorf is a village-level subdivision of the town of Usingen in the Hochtaunus district of Hesse, Germany.
-
D.
Ruhmannsfelden
Ruhmannsfelden is a small market town in the Bavarian Forest region of southeastern Germany.
-
E.
Oranienburger Vorstadt
Oranienburger Vorstadt is a historic neighborhood in central Berlin, known for its 19th-century urban fabric, cultural sites, and proximity to key political and intellectual centers of the city.
- 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: Kellinghausen-Siedlung Triple: [Rüthen, hasSubdivision, Kellinghausen-Siedlung]
Generated description
Kellinghausen-Siedlung is a residential district within the town of Rüthen in North Rhine-Westphalia, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kellinghausen-Siedlung Target entity description: Kellinghausen-Siedlung is a residential district within the town of Rüthen in North Rhine-Westphalia, Germany.
-
A.
Gartenstadt
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
-
B.
Niederschönhausen
Niederschönhausen is a residential district in the Berlin borough of Pankow, known for its historic villas, green spaces, and the former presidential residence Schloss Schönhausen.
-
C.
Wilhelmsdorf
Wilhelmsdorf is a village-level subdivision of the town of Usingen in the Hochtaunus district of Hesse, Germany.
-
D.
Ruhmannsfelden
Ruhmannsfelden is a small market town in the Bavarian Forest region of southeastern Germany.
-
E.
Oranienburger Vorstadt
Oranienburger Vorstadt is a historic neighborhood in central Berlin, known for its 19th-century urban fabric, cultural sites, and proximity to key political and intellectual centers of the city.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954b97a508190b6c901c506441dd0 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6559ba5108190b85be540a405eec8 |
completed | May 2, 2026, 7:50 p.m. |
| NEDg | Description generation | batch_69f6566fe5dc8190910bc7ad34593a58 |
completed | May 2, 2026, 7:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f65702435c8190a69e681c56a19b16 |
completed | May 2, 2026, 7:56 p.m. |
Created at: April 9, 2026, 5:02 p.m.