Triple
T15736831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kröpcke square |
E381492
|
entity |
| Predicate | hasNearbyStreet |
P8235
|
FINISHED |
| Object | Georgstraße |
E424141
|
NE FINISHED |
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: Georgstraße | Statement: [Kröpcke square, hasNearbyStreet, Georgstraße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Georgstraße Context triple: [Kröpcke square, hasNearbyStreet, Georgstraße]
-
A.
Georgstraße
chosen
Georgstraße is a major shopping and promenade street in the central district of Hanover, Germany, known for its retail stores, historic buildings, and cultural venues.
-
B.
Ludwigstraße
Ludwigstraße is a grand, historic boulevard in Munich, Germany, known for its monumental 19th-century architecture and role as one of the city’s main north–south axes.
-
C.
Golßener Straße
Golßener Straße is a street in Berlin, Germany, located in the Neukölln area near Columbiadamm and the former Tempelhof Airport grounds.
-
D.
Grunerstraße
Grunerstraße is a central street in Berlin located near Alexanderplatz, known for carrying heavy traffic through the city’s Mitte district.
-
E.
Kaufingerstraße
Kaufingerstraße is one of Munich’s main and oldest pedestrian shopping streets, lined with stores and historic buildings in the city center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fd6eb888190b7a9b07b76e62c0d |
completed | April 16, 2026, 2:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00cfb95b348190a006f699c01e85ce |
completed | May 10, 2026, 6:34 p.m. |
Created at: April 10, 2026, 4:46 a.m.