Triple
T17917572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prenzlauer Berg |
E447971
|
entity |
| Predicate | hasStreet |
P959
|
FINISHED |
| Object |
Danziger Straße
Danziger Straße is a major thoroughfare in Berlin’s Prenzlauer Berg district, known for its mix of historic buildings, shops, bars, and tram lines.
|
E1363278
|
NE FINISHED |
How this triple was built (4 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: Danziger Straße | Statement: [Prenzlauer Berg, hasStreet, Danziger Straße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danziger Straße Context triple: [Prenzlauer Berg, hasStreet, Danziger Straße]
-
A.
Rosenbergstraße
Rosenbergstraße is a street that lends its name to and hosts the Rosenbergstraße campus.
-
B.
Warschauer Straße
Warschauer Straße is a major Berlin transport hub and station complex in the Friedrichshain district, serving both U-Bahn and S-Bahn lines near the East Side Gallery.
-
C.
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.
-
D.
Chausseestraße
Chausseestraße is a major historic street in Berlin, Germany, known for its cultural landmarks and central location.
-
E.
Mauerstraße
Mauerstraße is a street in central Berlin, Germany, historically notable for running along the former course of the Berlin Wall near key government and commercial areas.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Danziger Straße Triple: [Prenzlauer Berg, hasStreet, Danziger Straße]
Generated description
Danziger Straße is a major thoroughfare in Berlin’s Prenzlauer Berg district, known for its mix of historic buildings, shops, bars, and tram lines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Danziger Straße Target entity description: Danziger Straße is a major thoroughfare in Berlin’s Prenzlauer Berg district, known for its mix of historic buildings, shops, bars, and tram lines.
-
A.
Rosenbergstraße
Rosenbergstraße is a street that lends its name to and hosts the Rosenbergstraße campus.
-
B.
Warschauer Straße
Warschauer Straße is a major Berlin transport hub and station complex in the Friedrichshain district, serving both U-Bahn and S-Bahn lines near the East Side Gallery.
-
C.
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.
-
D.
Chausseestraße
Chausseestraße is a major historic street in Berlin, Germany, known for its cultural landmarks and central location.
-
E.
Mauerstraße
Mauerstraße is a street in central Berlin, Germany, historically notable for running along the former course of the Berlin Wall near key government and commercial areas.
- F. None of above. chosen
Provenance (5 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a30778fc81908b5b2e308fb158a5 |
completed | April 19, 2026, 9:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a06f893ede48190a5089ef01ba1857a |
completed | May 15, 2026, 10:42 a.m. |
| NEDg | Description generation | batch_6a06fa3508a081908e76f7f865ca65ce |
completed | May 15, 2026, 10:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a06fb454edc8190a1489c04bc749852 |
completed | May 15, 2026, 10:53 a.m. |
Created at: April 10, 2026, 10:20 a.m.