Triple
T14333878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potsdam-Mittelmark |
E355420
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Brück
Brück is a small town in the Potsdam-Mittelmark district of the German state of Brandenburg.
|
E1094520
|
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: Brück | Statement: [Potsdam-Mittelmark, containsTown, Brück]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brück Context triple: [Potsdam-Mittelmark, containsTown, Brück]
-
A.
Bersenbrück
Bersenbrück is a small town in Lower Saxony, Germany, known for its historic abbey and its location on the river Hase.
-
B.
Kindelbrück
Kindelbrück is a small town in the German state of Thuringia, situated in the Unstrut river valley and known for its rural character and historical architecture.
-
C.
Hochbrück
Hochbrück is a district of the Bavarian town of Garching near Munich, known for its industrial areas and proximity to major research and technology facilities.
-
D.
Königsbrück
Königsbrück is a small town in the Free State of Saxony in eastern Germany, known for its historic architecture and proximity to the Königsbrück Heath nature reserve.
-
E.
Bruck
Bruck is a district of the Bavarian city of Erlangen in Germany, known for its residential areas and proximity to local industry and research institutions.
- 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: Brück Triple: [Potsdam-Mittelmark, containsTown, Brück]
Generated description
Brück is a small town in the Potsdam-Mittelmark district of the German state of Brandenburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brück Target entity description: Brück is a small town in the Potsdam-Mittelmark district of the German state of Brandenburg.
-
A.
Bersenbrück
Bersenbrück is a small town in Lower Saxony, Germany, known for its historic abbey and its location on the river Hase.
-
B.
Kindelbrück
Kindelbrück is a small town in the German state of Thuringia, situated in the Unstrut river valley and known for its rural character and historical architecture.
-
C.
Hochbrück
Hochbrück is a district of the Bavarian town of Garching near Munich, known for its industrial areas and proximity to major research and technology facilities.
-
D.
Königsbrück
Königsbrück is a small town in the Free State of Saxony in eastern Germany, known for its historic architecture and proximity to the Königsbrück Heath nature reserve.
-
E.
Bruck
Bruck is a district of the Bavarian city of Erlangen in Germany, known for its residential areas and proximity to local industry and research institutions.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8c20d2148190bb534bef338e871d |
completed | April 14, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c3d20688190973e37ca38b4afe0 |
completed | May 8, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fd4ce6a6ec8190b7a86aa44f6305f8 |
completed | May 8, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd4d5b489081908016e62d4db476ce |
completed | May 8, 2026, 2:41 a.m. |
Created at: April 10, 2026, 1:13 a.m.