Triple
T21287749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diepholz district |
E524705
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Barnstorf
Barnstorf is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Diepholz district.
|
E1476228
|
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: Barnstorf | Statement: [Diepholz district, contains, Barnstorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barnstorf Context triple: [Diepholz district, contains, Barnstorf]
-
A.
Sommerstorf
Sommerstorf is a small lakeside settlement in northeastern Germany situated on the shores of Kummerower See.
-
B.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
C.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
-
D.
Wildeshausen
Wildeshausen is a historic town in Lower Saxony, Germany, known for its medieval architecture and role as a regional administrative and cultural center.
-
E.
Mölln
Mölln is a historic town in northern Germany, known for its medieval center and association with the folk figure Till Eulenspiegel.
- 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: Barnstorf Triple: [Diepholz district, contains, Barnstorf]
Generated description
Barnstorf is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Diepholz district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barnstorf Target entity description: Barnstorf is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Diepholz district.
-
A.
Sommerstorf
Sommerstorf is a small lakeside settlement in northeastern Germany situated on the shores of Kummerower See.
-
B.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
C.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
-
D.
Wildeshausen
Wildeshausen is a historic town in Lower Saxony, Germany, known for its medieval architecture and role as a regional administrative and cultural center.
-
E.
Mölln
Mölln is a historic town in northern Germany, known for its medieval center and association with the folk figure Till Eulenspiegel.
- 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_69e0b5171f6c8190a5d57201ede73811 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e736d7c57c8190bc4180ea590a62d4 |
completed | April 21, 2026, 8:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a099809b3d88190b78ddeae641a7abf |
completed | May 17, 2026, 10:27 a.m. |
| NEDg | Description generation | batch_6a0998f27c6c81908a88a4a831445d57 |
completed | May 17, 2026, 10:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09996c8d7c81908cc9874f6e1a3fff |
completed | May 17, 2026, 10:33 a.m. |
Created at: April 16, 2026, 4:03 p.m.