Triple
T13137895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vienenburg |
E312129
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Weddingen
Weddingen is a village that forms one of the districts of the town of Vienenburg in Lower Saxony, Germany.
|
E1022204
|
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: Weddingen | Statement: [Vienenburg, hasSubdivision, Weddingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weddingen Context triple: [Vienenburg, hasSubdivision, Weddingen]
-
A.
Wettingen
Wettingen is a Swiss town in the canton of Aargau, located in the Limmat Valley near the city of Baden.
-
B.
Weiningen
Weiningen is a small Swiss municipality in the canton of Zurich, located in the Limmat Valley near the city of Zurich.
-
C.
Winningen
Winningen is a small wine-growing municipality on the Moselle River in western Germany, known for its picturesque vineyards and historic village character.
-
D.
Meerbusch
Meerbusch is a town in the German state of North Rhine-Westphalia, situated on the west bank of the Rhine near Düsseldorf and known for its affluent residential areas and green surroundings.
-
E.
Waldshut-Tiengen
Waldshut-Tiengen is a town in southwestern Germany near the Swiss border, formed by the merger of Waldshut and Tiengen and known for its historic old town and Rhine River setting.
- 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: Weddingen Triple: [Vienenburg, hasSubdivision, Weddingen]
Generated description
Weddingen is a village that forms one of the districts of the town of Vienenburg in Lower Saxony, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Weddingen Target entity description: Weddingen is a village that forms one of the districts of the town of Vienenburg in Lower Saxony, Germany.
-
A.
Wettingen
Wettingen is a Swiss town in the canton of Aargau, located in the Limmat Valley near the city of Baden.
-
B.
Weiningen
Weiningen is a small Swiss municipality in the canton of Zurich, located in the Limmat Valley near the city of Zurich.
-
C.
Winningen
Winningen is a small wine-growing municipality on the Moselle River in western Germany, known for its picturesque vineyards and historic village character.
-
D.
Meerbusch
Meerbusch is a town in the German state of North Rhine-Westphalia, situated on the west bank of the Rhine near Düsseldorf and known for its affluent residential areas and green surroundings.
-
E.
Waldshut-Tiengen
Waldshut-Tiengen is a town in southwestern Germany near the Swiss border, formed by the merger of Waldshut and Tiengen and known for its historic old town and Rhine River setting.
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b6a4348190b9922ed255759078 |
completed | April 10, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e295b3408190a7246115d3ee90e5 |
completed | May 3, 2026, 5:52 a.m. |
| NEDg | Description generation | batch_69f6e3a950548190836e24621a5ece74 |
completed | May 3, 2026, 5:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6e454f95c8190b023c5d141999dd8 |
completed | May 3, 2026, 5:59 a.m. |
Created at: April 9, 2026, 9:09 p.m.