Triple
T9495212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wagria |
E228986
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Dahme
Dahme is a small coastal town on the Baltic Sea in northern Germany, known for its beaches and seaside tourism.
|
E872836
|
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: Dahme | Statement: [Wagria, contains, Dahme]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dahme Context triple: [Wagria, contains, Dahme]
-
A.
Dahme
The Dahme is a river in eastern Germany that flows through Brandenburg and Berlin before joining the Spree.
-
B.
Oder-Spree
Oder-Spree is a rural district in the eastern German state of Brandenburg, known for its lakes, forests, and towns along the Oder and Spree rivers.
-
C.
River Spree
River Spree is a major river flowing through Berlin, Germany, known for shaping the city’s landscape and passing many historic and cultural landmarks.
-
D.
Unstrut River
The Unstrut River is a tributary of the Saale in central Germany, flowing through Thuringia and Saxony-Anhalt and known for its scenic valleys, vineyards, and historic towns.
-
E.
Peene River
The Peene River is a lowland river in northeastern Germany, often called the "Amazon of the North" for its largely untouched wetlands and rich biodiversity.
- 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: Dahme Triple: [Wagria, contains, Dahme]
Generated description
Dahme is a small coastal town on the Baltic Sea in northern Germany, known for its beaches and seaside tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dahme Target entity description: Dahme is a small coastal town on the Baltic Sea in northern Germany, known for its beaches and seaside tourism.
-
A.
Dahme
The Dahme is a river in eastern Germany that flows through Brandenburg and Berlin before joining the Spree.
-
B.
Oder-Spree
Oder-Spree is a rural district in the eastern German state of Brandenburg, known for its lakes, forests, and towns along the Oder and Spree rivers.
-
C.
River Spree
River Spree is a major river flowing through Berlin, Germany, known for shaping the city’s landscape and passing many historic and cultural landmarks.
-
D.
Unstrut River
The Unstrut River is a tributary of the Saale in central Germany, flowing through Thuringia and Saxony-Anhalt and known for its scenic valleys, vineyards, and historic towns.
-
E.
Peene River
The Peene River is a lowland river in northeastern Germany, often called the "Amazon of the North" for its largely untouched wetlands and rich biodiversity.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd95eb87b081908fc7255598cd9a24 |
completed | April 1, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94aceb7108190beeec78587c04161 |
completed | April 10, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69d94c6fa9ac8190819a399754d2bd15 |
completed | April 10, 2026, 7:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d953440a508190a50d1897cdbeba03 |
completed | April 10, 2026, 7:45 p.m. |
Created at: March 30, 2026, 7:56 p.m.