Triple
T10950103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herbertstraße |
E258702
|
entity |
| Predicate | hasLocalName |
P6353
|
FINISHED |
| Object | Herbertstrasse |
E258702
|
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: Herbertstrasse | Statement: [Herbertstraße, hasLocalName, Herbertstrasse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herbertstrasse Context triple: [Herbertstraße, hasLocalName, Herbertstrasse]
-
A.
Herbertstraße
chosen
Herbertstraße is a short, gated street in Hamburg’s St. Pauli district known as one of Germany’s most famous red-light prostitution streets.
-
B.
Hermannstraße
Hermannstraße is a Berlin railway and U-Bahn station in the Neukölln district that serves as a key interchange point on the city’s Ringbahn network.
-
C.
Bergmannstraße
Bergmannstraße is a notable street in Berlin, Germany, known for its lively mix of cafés, shops, and historic sites including the Luisenstädtischer Friedhof cemetery.
-
D.
Beusselstraße
Beusselstraße is a railway station in Berlin that serves the city's circular Ringbahn line and connects the surrounding Moabit area to the wider S-Bahn network.
-
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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770ed2f1c819081ec58457f57889d |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3447d8cc88190a3e28f204a93a7d3 |
completed | April 18, 2026, 8:44 a.m. |
Created at: April 8, 2026, 9:23 p.m.