Triple
T2337481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elbphilharmonie |
E44343
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | State of Hamburg |
E7419
|
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: State of Hamburg | Statement: [Elbphilharmonie, region, State of Hamburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: State of Hamburg Context triple: [Elbphilharmonie, region, State of Hamburg]
-
A.
federal state of Berlin
The federal state of Berlin is both Germany’s capital city and one of its 16 constituent states, functioning as a major political, cultural, and economic center in Europe.
-
B.
Bremen
Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
-
C.
Hamburg metropolitan region
The Hamburg metropolitan region is a major economic and population center in northern Germany, anchored by the city of Hamburg and its extensive port and logistics industries.
-
D.
Hamburg
chosen
Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
-
E.
Lower Saxony
Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc68ac4348190ab6ec46ec7879643 |
completed | March 7, 2026, 6:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae961adfdc8190bf79d479d8207599 |
completed | March 9, 2026, 9:42 a.m. |
Created at: March 4, 2026, 7:51 p.m.