Triple
T8688000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dießen am Ammersee |
E206211
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Dettenschwang
Dettenschwang is a village and district of the market town Dießen am Ammersee in the Bavarian region of Germany.
|
E752213
|
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: Dettenschwang | Statement: [Dießen am Ammersee, hasSubdivision, Dettenschwang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dettenschwang Context triple: [Dießen am Ammersee, hasSubdivision, Dettenschwang]
-
A.
Schmutter
The Schmutter is a river in Bavaria, Germany, known as a regional tributary that flows through the Swabian landscape before joining the Wertach.
-
B.
Kehrsatz
Kehrsatz is a municipality in the canton of Bern, Switzerland, situated just south of the city of Bern within its metropolitan region.
-
C.
Schüpfen
Schüpfen is a municipality in the canton of Bern in Switzerland, located in the Seeland administrative district.
-
D.
Zesgehuchten
Zesgehuchten was a former village and municipality in the Dutch province of North Brabant, now part of the city of Geldrop-Mierlo.
-
E.
Flaemmchen
Flaemmchen is a young, ambitious stenographer and aspiring actress in Vicki Baum’s novel (and its film adaptation) "Grand Hotel," representing the struggles and dreams of working-class women in Weimar-era Berlin.
- 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: Dettenschwang Triple: [Dießen am Ammersee, hasSubdivision, Dettenschwang]
Generated description
Dettenschwang is a village and district of the market town Dießen am Ammersee in the Bavarian region of Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dettenschwang Target entity description: Dettenschwang is a village and district of the market town Dießen am Ammersee in the Bavarian region of Germany.
-
A.
Schmutter
The Schmutter is a river in Bavaria, Germany, known as a regional tributary that flows through the Swabian landscape before joining the Wertach.
-
B.
Kehrsatz
Kehrsatz is a municipality in the canton of Bern, Switzerland, situated just south of the city of Bern within its metropolitan region.
-
C.
Schüpfen
Schüpfen is a municipality in the canton of Bern in Switzerland, located in the Seeland administrative district.
-
D.
Zesgehuchten
Zesgehuchten was a former village and municipality in the Dutch province of North Brabant, now part of the city of Geldrop-Mierlo.
-
E.
Flaemmchen
Flaemmchen is a young, ambitious stenographer and aspiring actress in Vicki Baum’s novel (and its film adaptation) "Grand Hotel," representing the struggles and dreams of working-class women in Weimar-era Berlin.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5731cf08819082e0cbe0975b70bb |
completed | March 31, 2026, 11:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef3d6904c8190a8456a99dae87bf2 |
completed | April 2, 2026, 10:55 p.m. |
| NEDg | Description generation | batch_69cef56df85c81908cb10aa7d80ce358 |
completed | April 2, 2026, 11:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cef6757cd8819082ffc32256b0f86c |
completed | April 2, 2026, 11:06 p.m. |
Created at: March 30, 2026, 6:33 p.m.