Triple
T12385515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erlangen-Höchstadt |
E295851
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Weisendorf
Weisendorf is a small municipality in the Erlangen-Höchstadt district of Bavaria, Germany, known for its rural character and proximity to the city of Erlangen.
|
E1001332
|
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: Weisendorf | Statement: [Erlangen-Höchstadt, containsMunicipality, Weisendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weisendorf Context triple: [Erlangen-Höchstadt, containsMunicipality, Weisendorf]
-
A.
Neuendorf
Neuendorf is a small village on the Baltic Sea island of Hiddensee in Germany, known for its traditional thatched houses and maritime character.
-
B.
Nordendorf
Nordendorf is a small municipality in Bavaria, Germany, situated within the Augsburg district.
-
C.
Wilhelmsdorf
Wilhelmsdorf is a village-level subdivision of the town of Usingen in the Hochtaunus district of Hesse, Germany.
-
D.
Landersdorf
Landersdorf is a locality within the city of Krems an der Donau in Lower Austria, known as part of its surrounding wine-growing and rural area.
-
E.
Westendorf
Westendorf is a popular Austrian alpine village known for its skiing, hiking, and picturesque mountain scenery.
- 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: Weisendorf Triple: [Erlangen-Höchstadt, containsMunicipality, Weisendorf]
Generated description
Weisendorf is a small municipality in the Erlangen-Höchstadt district of Bavaria, Germany, known for its rural character and proximity to the city of Erlangen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Weisendorf Target entity description: Weisendorf is a small municipality in the Erlangen-Höchstadt district of Bavaria, Germany, known for its rural character and proximity to the city of Erlangen.
-
A.
Neuendorf
Neuendorf is a small village on the Baltic Sea island of Hiddensee in Germany, known for its traditional thatched houses and maritime character.
-
B.
Nordendorf
Nordendorf is a small municipality in Bavaria, Germany, situated within the Augsburg district.
-
C.
Wilhelmsdorf
Wilhelmsdorf is a village-level subdivision of the town of Usingen in the Hochtaunus district of Hesse, Germany.
-
D.
Landersdorf
Landersdorf is a locality within the city of Krems an der Donau in Lower Austria, known as part of its surrounding wine-growing and rural area.
-
E.
Westendorf
Westendorf is a popular Austrian alpine village known for its skiing, hiking, and picturesque mountain scenery.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbd489c819098233a111442762e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c648b948190bfc6032cf8fdd7d5 |
completed | May 2, 2026, 10:36 p.m. |
| NEDg | Description generation | batch_69f6807200d4819081ab83b4b5afd9a4 |
completed | May 2, 2026, 10:53 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f681028aa8819099f47625c86d8a4a |
completed | May 2, 2026, 10:56 p.m. |
Created at: April 8, 2026, 9:54 p.m.