Triple
T14266178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U9 |
E353649
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Nauener Platz
Nauener Platz is a Berlin U-Bahn station on the U9 line located in the Wedding district of the city.
|
E1098515
|
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: Nauener Platz | Statement: [U9, hasStation, Nauener Platz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nauener Platz Context triple: [U9, hasStation, Nauener Platz]
-
A.
Boxhagener Platz
Boxhagener Platz is a popular square and park in Berlin’s Friedrichshain district, known for its lively weekend flea market, bars, and cafés.
-
B.
Kaulbachplatz
Kaulbachplatz is an underground station on Munich’s U-Bahn network, serving the U3 line in the Schwabing district.
-
C.
Savignyplatz
Savignyplatz is a well-known square and surrounding neighborhood in Berlin’s Charlottenburg district, noted for its lively cafés, restaurants, and historic urban charm.
-
D.
Adenauerplatz
Adenauerplatz is a prominent public square and transport hub in Berlin named after Germany’s first post-war chancellor, Konrad Adenauer.
-
E.
Hermannplatz
Hermannplatz is a major Berlin U-Bahn station and transport hub in the Neukölln district, known for its historic architecture and busy commercial 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: Nauener Platz Triple: [U9, hasStation, Nauener Platz]
Generated description
Nauener Platz is a Berlin U-Bahn station on the U9 line located in the Wedding district of the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nauener Platz Target entity description: Nauener Platz is a Berlin U-Bahn station on the U9 line located in the Wedding district of the city.
-
A.
Boxhagener Platz
Boxhagener Platz is a popular square and park in Berlin’s Friedrichshain district, known for its lively weekend flea market, bars, and cafés.
-
B.
Kaulbachplatz
Kaulbachplatz is an underground station on Munich’s U-Bahn network, serving the U3 line in the Schwabing district.
-
C.
Savignyplatz
Savignyplatz is a well-known square and surrounding neighborhood in Berlin’s Charlottenburg district, noted for its lively cafés, restaurants, and historic urban charm.
-
D.
Adenauerplatz
Adenauerplatz is a prominent public square and transport hub in Berlin named after Germany’s first post-war chancellor, Konrad Adenauer.
-
E.
Hermannplatz
Hermannplatz is a major Berlin U-Bahn station and transport hub in the Neukölln district, known for its historic architecture and busy commercial 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6358c2288190ac1fd26e688a605d |
completed | April 14, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bb7e0ac81909d62993689b56f83 |
completed | May 8, 2026, 3:42 a.m. |
| NEDg | Description generation | batch_69fd5cc62c248190bb280bc095ba6153 |
completed | May 8, 2026, 3:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd5d5bdfa48190a93cce877854bc0e |
completed | May 8, 2026, 3:49 a.m. |
Created at: April 10, 2026, 1:09 a.m.