Triple
T16050779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U-Bahnhof Schloßstraße |
E389345
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Schloßstraße |
—
|
NE NERFINISHED |
How this triple was built (2 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: Schloßstraße | Statement: [U-Bahnhof Schloßstraße, hasName, Schloßstraße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schloßstraße Context triple: [U-Bahnhof Schloßstraße, hasName, Schloßstraße]
-
A.
Schloßstraße
chosen
Schloßstraße is a Berlin U-Bahn station on the U9 line located in the Steglitz district, serving as a key access point to the nearby Schlossstraße shopping area.
-
B.
Burgenstraße
Burgenstraße is a famous German tourist route known for connecting numerous historic castles and picturesque medieval towns.
-
C.
Burgstraße
Burgstraße is a historic street located in the Old Town (Altstadt) of Hanover, Germany, known for its traditional architecture and central location.
-
D.
Kaufingerstraße
Kaufingerstraße is one of Munich’s main and oldest pedestrian shopping streets, lined with stores and historic buildings in the city center.
-
E.
Scharnweberstraße
Scharnweberstraße is a station on Berlin’s U6 U-Bahn line serving the Reinickendorf district in the north of the city.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18361c31481908b253e8b814ec9f6 |
completed | April 17, 2026, 12:48 a.m. |
Created at: April 10, 2026, 4:56 a.m.