Triple
T10950778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Groß Borstel |
E258719
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Hamburg-Nord |
E902274
|
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: Hamburg-Nord | Statement: [Groß Borstel, partOf, Hamburg-Nord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamburg-Nord Context triple: [Groß Borstel, partOf, Hamburg-Nord]
-
A.
Hamburg-Nord
chosen
Hamburg-Nord is a central borough of the German city-state of Hamburg, comprising several districts and neighborhoods including Fuhlsbüttel.
-
B.
Hamburg-Finkenwerder
Hamburg-Finkenwerder is a district of Hamburg, Germany, known for its historic and ongoing role in shipbuilding and aviation industries along the River Elbe.
-
C.
Fuhlsbüttel
Fuhlsbüttel is a district in the northern German city of Hamburg best known for hosting the city’s international airport.
-
D.
Bremen-Nord
Bremen-Nord is the northern district of the German city-state of Bremen, comprising several suburban and historically maritime-oriented neighborhoods along the Weser River.
-
E.
Hamburg-Mitte
Hamburg-Mitte is the central borough of Hamburg, Germany, encompassing the historic city center, major commercial areas, and key cultural and political institutions.
- 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_69d770fc156c8190826e124c13ce7242 |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d4f9518819091f7e02ad5f63ab3 |
completed | April 19, 2026, 1:18 a.m. |
Created at: April 8, 2026, 9:23 p.m.