Triple
T31083172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nürnberger Straße |
E792156
|
entity |
| Predicate | nearShoppingDistrict |
P61270
|
FINISHED |
| Object | Kurfürstendamm / Tauentzienstraße shopping area |
—
|
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: Kurfürstendamm / Tauentzienstraße shopping area | Statement: [Nürnberger Straße, nearShoppingDistrict, Kurfürstendamm / Tauentzienstraße shopping area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearShoppingDistrict Context triple: [Nürnberger Straße, nearShoppingDistrict, Kurfürstendamm / Tauentzienstraße shopping area]
-
A.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
C.
hasShoppingDistrictName
Indicates that an entity’s shopping district is identified by a specific name.
-
D.
nearDowntown
Indicates that one location is situated close to or within a short distance of a city’s downtown area.
-
E.
nearbyLocation
chosen
Indicates that one location is situated close to another location in physical space.
- F. None of above.
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_69f224ce48348190bd0fc23f656ed683 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f695fb5f60819099ac1f20472cfc61 |
completed | May 3, 2026, 12:25 a.m. |
| PD | Predicate disambiguation | batch_69f690f13d7481908ddfefe95df2a1c2 |
completed | May 3, 2026, 12:04 a.m. |
Created at: April 29, 2026, 9:02 p.m.