Triple
T9085021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weidendammer Bridge |
E217729
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Friedrichstraße |
E73425
|
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: Friedrichstraße | Statement: [Weidendammer Bridge, near, Friedrichstraße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Friedrichstraße Context triple: [Weidendammer Bridge, near, Friedrichstraße]
-
A.
Friedrichstraße
chosen
Friedrichstraße is a major central Berlin transport hub and historic thoroughfare known for its shopping, cultural venues, and role as a former border crossing during the Cold War.
-
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.
Chausseestraße
Chausseestraße is a major historic street in Berlin, Germany, known for its cultural landmarks and central location.
-
D.
Maximilianstraße
Maximilianstraße is one of Munich’s most famous boulevards, known for its luxury boutiques, grand architecture, and cultural landmarks.
-
E.
Leipziger Straße
Leipziger Straße is a major historic thoroughfare in central Berlin, known for its government buildings, commercial centers, and role in the city’s urban core.
- 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_69ca83d7a0388190ba1af89ed7ba36f9 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc960d0b008190b0e9e61fac45101d |
completed | April 1, 2026, 3:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cffe38f0048190a9fb15e73d9bcd50 |
completed | April 3, 2026, 5:51 p.m. |
Created at: March 30, 2026, 7:13 p.m.