Triple
T20481988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stuttgart Schwabstraße |
E502477
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Schwabstraße
Schwabstraße is a street in Stuttgart, Germany, known for giving its name to the nearby Stuttgart Schwabstraße railway station.
|
E1434424
|
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: Schwabstraße | Statement: [Stuttgart Schwabstraße, namedAfter, Schwabstraße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwabstraße Context triple: [Stuttgart Schwabstraße, namedAfter, Schwabstraße]
-
A.
Schwartzkopffstraße
Schwartzkopffstraße is a Berlin U-Bahn station on the U6 line located in the central district of the city.
-
B.
Scharnweberstraße
Scharnweberstraße is a station on Berlin’s U6 U-Bahn line serving the Reinickendorf district in the north of the city.
-
C.
Otisstraße
Otisstraße is a Berlin U-Bahn station on line U6 located in the Reinickendorf district of the city.
-
D.
Brienner Straße
Brienner Straße is a historic boulevard in Munich, Germany, known for its neoclassical architecture and its role as one of the city’s grand royal avenues.
-
E.
Sambesistraße
Sambesistraße is a street in Berlin’s Afrikanisches Viertel, a neighborhood known for roads named after African regions, rivers, and countries.
- 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: Schwabstraße Triple: [Stuttgart Schwabstraße, namedAfter, Schwabstraße]
Generated description
Schwabstraße is a street in Stuttgart, Germany, known for giving its name to the nearby Stuttgart Schwabstraße railway station.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schwabstraße Target entity description: Schwabstraße is a street in Stuttgart, Germany, known for giving its name to the nearby Stuttgart Schwabstraße railway station.
-
A.
Schwartzkopffstraße
Schwartzkopffstraße is a Berlin U-Bahn station on the U6 line located in the central district of the city.
-
B.
Scharnweberstraße
Scharnweberstraße is a station on Berlin’s U6 U-Bahn line serving the Reinickendorf district in the north of the city.
-
C.
Otisstraße
Otisstraße is a Berlin U-Bahn station on line U6 located in the Reinickendorf district of the city.
-
D.
Brienner Straße
Brienner Straße is a historic boulevard in Munich, Germany, known for its neoclassical architecture and its role as one of the city’s grand royal avenues.
-
E.
Sambesistraße
Sambesistraße is a street in Berlin’s Afrikanisches Viertel, a neighborhood known for roads named after African regions, rivers, and countries.
- 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_69e0b4af32848190aea80682b44d5d6e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69b57fa9c819091d12320d46a0cee |
completed | April 20, 2026, 9:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0893ae04f481908da8206dfba0b463 |
completed | May 16, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_6a0894c4c8b88190b2ac594477811c7b |
completed | May 16, 2026, 4:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08954be2688190a51911d6922a6413 |
completed | May 16, 2026, 4:03 p.m. |
Created at: April 16, 2026, 11:34 a.m.