Triple
T10988074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Kissingen (district) |
E259682
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Wildflecken
Wildflecken is a municipality in northern Bavaria, Germany, known for its location in the Rhön Mountains and its history as a former military training area.
|
E898318
|
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: Wildflecken | Statement: [Bad Kissingen (district), contains, Wildflecken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wildflecken Context triple: [Bad Kissingen (district), contains, Wildflecken]
-
A.
Wilding
Wilding is an English surname borne by various notable individuals in the arts, sports, and public life.
-
B.
Fischeln
Fischeln is a district of the German city of Krefeld in the state of North Rhine-Westphalia.
-
C.
Vildgräs
Vildgräs is a musical number from the Swedish stage musical "Kristina från Duvemåla," composed by Benny Andersson and Björn Ulvaeus and based on Vilhelm Moberg’s emigrant novels.
-
D.
Steinwiesen
Steinwiesen is a small municipality in northern Bavaria, Germany, known for its location in the Franconian Forest and its traditional rural character.
-
E.
Bleckede
Bleckede is a small town in Lower Saxony, Germany, situated on the Elbe River and known for its historic architecture and natural surroundings.
- 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: Wildflecken Triple: [Bad Kissingen (district), contains, Wildflecken]
Generated description
Wildflecken is a municipality in northern Bavaria, Germany, known for its location in the Rhön Mountains and its history as a former military training area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wildflecken Target entity description: Wildflecken is a municipality in northern Bavaria, Germany, known for its location in the Rhön Mountains and its history as a former military training area.
-
A.
Wilding
Wilding is an English surname borne by various notable individuals in the arts, sports, and public life.
-
B.
Fischeln
Fischeln is a district of the German city of Krefeld in the state of North Rhine-Westphalia.
-
C.
Vildgräs
Vildgräs is a musical number from the Swedish stage musical "Kristina från Duvemåla," composed by Benny Andersson and Björn Ulvaeus and based on Vilhelm Moberg’s emigrant novels.
-
D.
Steinwiesen
Steinwiesen is a small municipality in northern Bavaria, Germany, known for its location in the Franconian Forest and its traditional rural character.
-
E.
Bleckede
Bleckede is a small town in Lower Saxony, Germany, situated on the Elbe River and known for its historic architecture and natural surroundings.
- 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_69e344f95ab88190bbce8f0eab0b2713 |
completed | April 18, 2026, 8:46 a.m. |
| NEDg | Description generation | batch_69e3556e8b408190a02a1fe194ae5750 |
completed | April 18, 2026, 9:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3591ecd548190b049ce95fe3f86d9 |
completed | April 18, 2026, 10:12 a.m. |
Created at: April 8, 2026, 9:24 p.m.