Triple
T9630569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg Dungeon |
E232792
|
entity |
| Predicate | locatedInDistrict |
P40
|
FINISHED |
| Object | Hamburg-Mitte |
E232790
|
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-Mitte | Statement: [Hamburg Dungeon, locatedInDistrict, Hamburg-Mitte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamburg-Mitte Context triple: [Hamburg Dungeon, locatedInDistrict, Hamburg-Mitte]
-
A.
Hamburg-Mitte
chosen
Hamburg-Mitte is the central borough of Hamburg, Germany, encompassing the historic city center, major commercial areas, and key cultural and political institutions.
-
B.
Bornheim Mitte
Bornheim Mitte is a central public transit station in Frankfurt’s Bornheim district, serving as a key stop on the city’s U-Bahn network.
-
C.
Fuhlsbüttel
Fuhlsbüttel is a district in the northern German city of Hamburg best known for hosting the city’s international airport.
-
D.
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.
-
E.
Bahnhofsviertel
Bahnhofsviertel is a central Frankfurt district known for its mix of historic Wilhelminian architecture, nightlife, red-light area, and growing creative and gastronomic scene.
- 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_69ca848940cc8190b97cec654cb3bb4a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b01863c8190a9ec4684804f96bc |
completed | April 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1822e12b8819089d4a64a9980cfcd |
completed | April 4, 2026, 9:27 p.m. |
Created at: March 30, 2026, 8:11 p.m.