Triple
T10988169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kitzingen (district) |
E259684
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Prichsenstadt
Prichsenstadt is a small historic town in northern Bavaria, Germany, known for its well-preserved medieval architecture and location within the Franconian wine region.
|
E992238
|
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: Prichsenstadt | Statement: [Kitzingen (district), hasMunicipality, Prichsenstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prichsenstadt Context triple: [Kitzingen (district), hasMunicipality, Prichsenstadt]
-
A.
Perasdorf
Perasdorf is a small municipality in the Straubing-Bogen district of Lower Bavaria, Germany.
-
B.
Dornstadt
Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
-
C.
Seelingstädt
Seelingstädt is a village and subdivision of the town of Trebsen in the German state of Saxony.
-
D.
Burgkunstadt
Burgkunstadt is a small Bavarian town in northern Germany known for its historic center and location in the Upper Franconia region.
-
E.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
- 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: Prichsenstadt Triple: [Kitzingen (district), hasMunicipality, Prichsenstadt]
Generated description
Prichsenstadt is a small historic town in northern Bavaria, Germany, known for its well-preserved medieval architecture and location within the Franconian wine region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Prichsenstadt Target entity description: Prichsenstadt is a small historic town in northern Bavaria, Germany, known for its well-preserved medieval architecture and location within the Franconian wine region.
-
A.
Perasdorf
Perasdorf is a small municipality in the Straubing-Bogen district of Lower Bavaria, Germany.
-
B.
Dornstadt
Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
-
C.
Seelingstädt
Seelingstädt is a village and subdivision of the town of Trebsen in the German state of Saxony.
-
D.
Burgkunstadt
Burgkunstadt is a small Bavarian town in northern Germany known for its historic center and location in the Upper Franconia region.
-
E.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d787b574d08190adec34b814a26437 |
completed | April 9, 2026, 11:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65e9136bc8190b35685376da7007e |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f660bc541c8190a4d1d7a4cc959ecf |
completed | May 2, 2026, 8:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6617997188190bfce14c54619af7f |
completed | May 2, 2026, 8:41 p.m. |
Created at: April 8, 2026, 9:24 p.m.