Triple
T9961677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rheinturm |
E195583
|
entity |
| Predicate | offersViewOf |
P3821
|
FINISHED |
| Object | MedienHafen |
E193726
|
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: MedienHafen | Statement: [Rheinturm, offersViewOf, MedienHafen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MedienHafen Context triple: [Rheinturm, offersViewOf, MedienHafen]
-
A.
MedienHafen
chosen
MedienHafen is a redeveloped former harbor area in Düsseldorf known for its striking contemporary architecture, media and creative industries, and vibrant waterfront nightlife.
-
B.
Heimathafen Neukölln
Heimathafen Neukölln is a cultural venue and theater in Berlin known for its diverse program of contemporary performances, concerts, and community-focused events.
-
C.
Ohlsdorf, Hamburg
Ohlsdorf, Hamburg is a northern district of Hamburg, Germany, best known for containing one of the world’s largest rural cemeteries, Ohlsdorf Cemetery.
-
D.
Fuhlsbüttel
Fuhlsbüttel is a district in the northern German city of Hamburg best known for hosting the city’s international airport.
-
E.
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.
- 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_69ca82ebd1288190912f9e4482d1fa35 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb6d37f0c8190946b958c399f3250 |
completed | April 2, 2026, 12:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d904bbc8190ac0b28600ed2e709 |
completed | April 5, 2026, 10:46 a.m. |
Created at: March 30, 2026, 8:47 p.m.