Triple
T13489273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S-Bahn line S1 |
E318589
|
entity |
| Predicate | via |
P5680
|
FINISHED |
| Object |
Frohnau
Frohnau is a residential district in northern Berlin known for its garden city layout and good public transport connections.
|
E1056393
|
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: Frohnau | Statement: [S-Bahn line S1, via, Frohnau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frohnau Context triple: [S-Bahn line S1, via, Frohnau]
-
A.
Roßdorf
Roßdorf is a municipality in the German state of Hesse, located near the city of Darmstadt.
-
B.
Wilmersdorf
Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
-
C.
Friedrichsfelde
Friedrichsfelde is a residential district in the Berlin borough of Lichtenberg, known for its large housing estates and proximity to Tierpark Berlin.
-
D.
Schönewalde
Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
-
E.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
- 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: Frohnau Triple: [S-Bahn line S1, via, Frohnau]
Generated description
Frohnau is a residential district in northern Berlin known for its garden city layout and good public transport connections.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frohnau Target entity description: Frohnau is a residential district in northern Berlin known for its garden city layout and good public transport connections.
-
A.
Roßdorf
Roßdorf is a municipality in the German state of Hesse, located near the city of Darmstadt.
-
B.
Wilmersdorf
Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
-
C.
Friedrichsfelde
Friedrichsfelde is a residential district in the Berlin borough of Lichtenberg, known for its large housing estates and proximity to Tierpark Berlin.
-
D.
Schönewalde
Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
-
E.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf3cbe2081908c6792362c67c8f1 |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d3afa0c81908733f3fd193d4e0f |
completed | May 3, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69f79e0518188190891bf4336630d0a5 |
completed | May 3, 2026, 7:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f79e7d50a88190b2a90094dd6d48ad |
completed | May 3, 2026, 7:14 p.m. |
Created at: April 9, 2026, 9:43 p.m.