Triple
T17546633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akazienstraße |
E427341
|
entity |
| Predicate | hasSideStreets |
P36837
|
FINISHED |
| Object | Belziger Straße |
—
|
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: Belziger Straße | Statement: [Akazienstraße, hasSideStreets, Belziger Straße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belziger Straße Context triple: [Akazienstraße, hasSideStreets, Belziger Straße]
-
A.
Belziger Straße
chosen
Belziger Straße is a street located in Berlin’s Akazienkiez neighborhood, known for its residential character and proximity to the area’s cafés and small shops.
-
B.
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.
-
C.
Treitlstraße
Treitlstraße is a street in central Vienna, Austria, located near Karlsplatz and the Vienna University of Technology.
-
D.
Berger Straße
Berger Straße is a prominent and lively shopping and dining street in Frankfurt am Main, known for its mix of boutiques, cafés, bars, and traditional Hessian pubs.
-
E.
Elbestraße
Elbestraße is a street in Frankfurt am Main’s central Bahnhofsviertel district, known for its nightlife, diverse culture, and proximity to the main train station.
- 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e454626cfc8190a2602ba4934b8e6d |
completed | April 19, 2026, 4:04 a.m. |
Created at: April 10, 2026, 5:49 a.m.