Triple
T13754625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donau-Ries |
E330443
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Amerdingen
Amerdingen is a small rural municipality in Bavaria, Germany, known for its agricultural landscape and traditional village character.
|
E1066023
|
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: Amerdingen | Statement: [Donau-Ries, containsMunicipality, Amerdingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amerdingen Context triple: [Donau-Ries, containsMunicipality, Amerdingen]
-
A.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
B.
Lauterhofen
Lauterhofen is a market town in Bavaria, Germany, known for its rural character and location within the Upper Palatinate region.
-
C.
Matzingen
Matzingen is a municipality in the canton of Thurgau in northeastern Switzerland.
-
D.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
E.
Hammelburg
Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
- 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: Amerdingen Triple: [Donau-Ries, containsMunicipality, Amerdingen]
Generated description
Amerdingen is a small rural municipality in Bavaria, Germany, known for its agricultural landscape and traditional village character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amerdingen Target entity description: Amerdingen is a small rural municipality in Bavaria, Germany, known for its agricultural landscape and traditional village character.
-
A.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
B.
Lauterhofen
Lauterhofen is a market town in Bavaria, Germany, known for its rural character and location within the Upper Palatinate region.
-
C.
Matzingen
Matzingen is a municipality in the canton of Thurgau in northeastern Switzerland.
-
D.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
E.
Hammelburg
Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02179c948190a652cc8c586e418f |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c0ded1e0819097ef42533357caf8 |
completed | May 3, 2026, 9:40 p.m. |
| NEDg | Description generation | batch_69f7c1e73fb481909f89ab3c0e9fb7d0 |
completed | May 3, 2026, 9:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7c3396f7c8190987079bf24ac8695 |
completed | May 3, 2026, 9:50 p.m. |
Created at: April 9, 2026, 10:09 p.m.