Triple
T14496479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Württemberg-Winnental |
E359512
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object |
Winnental
Winnental is a historical town that served as the capital of the former German territory of Württemberg-Winnental.
|
E1107876
|
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: Winnental | Statement: [Württemberg-Winnental, hasCapital, Winnental]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Winnental Context triple: [Württemberg-Winnental, hasCapital, Winnental]
-
A.
Waltershof
Waltershof is an industrial and port district of Hamburg, Germany, located within the borough of Hamburg-Mitte.
-
B.
Tannheim
Tannheim is a small municipality in the district of Biberach in the German state of Baden-Württemberg, known for its rural character and Swabian cultural heritage.
-
C.
Worb
Worb is a municipality in the canton of Bern in Switzerland, known for its historic village center and proximity to the city of Bern.
-
D.
Kiental
Kiental is a picturesque alpine valley and village in the Bernese Oberland region of Switzerland, known for its dramatic mountain scenery and hiking opportunities.
-
E.
Wollerau
Wollerau is an affluent municipality on the shores of Lake Zurich in the Swiss canton of Schwyz, known for its low taxes and as a residence of several wealthy individuals.
- 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: Winnental Triple: [Württemberg-Winnental, hasCapital, Winnental]
Generated description
Winnental is a historical town that served as the capital of the former German territory of Württemberg-Winnental.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Winnental Target entity description: Winnental is a historical town that served as the capital of the former German territory of Württemberg-Winnental.
-
A.
Waltershof
Waltershof is an industrial and port district of Hamburg, Germany, located within the borough of Hamburg-Mitte.
-
B.
Tannheim
Tannheim is a small municipality in the district of Biberach in the German state of Baden-Württemberg, known for its rural character and Swabian cultural heritage.
-
C.
Worb
Worb is a municipality in the canton of Bern in Switzerland, known for its historic village center and proximity to the city of Bern.
-
D.
Kiental
Kiental is a picturesque alpine valley and village in the Bernese Oberland region of Switzerland, known for its dramatic mountain scenery and hiking opportunities.
-
E.
Wollerau
Wollerau is an affluent municipality on the shores of Lake Zurich in the Swiss canton of Schwyz, known for its low taxes and as a residence of several wealthy individuals.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de93109cb081909a6e846db23a4635 |
completed | April 14, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94a7187c81909f173c2fb70509f5 |
completed | May 8, 2026, 7:45 a.m. |
| NEDg | Description generation | batch_69fd96860ba48190af21941197a97c97 |
completed | May 8, 2026, 7:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd974aa0688190916c8d79a68809ee |
completed | May 8, 2026, 7:56 a.m. |
Created at: April 10, 2026, 1:21 a.m.