Triple
T9630528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg-Mitte |
E232790
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Wilhelmsburg
Wilhelmsburg is a large island district of Hamburg, Germany, known for its diverse population, industrial areas, and extensive green spaces along the Elbe River.
|
E812025
|
NE FINISHED |
How this triple was built (4 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: Wilhelmsburg | Statement: [Hamburg-Mitte, contains, Wilhelmsburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilhelmsburg Context triple: [Hamburg-Mitte, contains, Wilhelmsburg]
-
A.
Brunsbüttel
Brunsbüttel is a German port town at the western entrance of the Kiel Canal on the North Sea coast of Schleswig-Holstein.
-
B.
Nittendorf
Nittendorf is a municipality in the Upper Palatinate region of Bavaria, Germany, situated west of the city of Regensburg.
-
C.
Berkheim
Berkheim is a small municipality in the district of Biberach in the federal state of Baden-Württemberg in southern Germany.
-
D.
Wilhelminaoord
Wilhelminaoord is a village in the Dutch province of Drenthe, known for its origins as a 19th-century welfare colony and its inclusion in the UNESCO-listed Colonies of Benevolence.
-
E.
Wilhelmsdorf
Wilhelmsdorf is a village-level subdivision of the town of Usingen in the Hochtaunus district of Hesse, Germany.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wilhelmsburg Triple: [Hamburg-Mitte, contains, Wilhelmsburg]
Generated description
Wilhelmsburg is a large island district of Hamburg, Germany, known for its diverse population, industrial areas, and extensive green spaces along the Elbe River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wilhelmsburg Target entity description: Wilhelmsburg is a large island district of Hamburg, Germany, known for its diverse population, industrial areas, and extensive green spaces along the Elbe River.
-
A.
Brunsbüttel
Brunsbüttel is a German port town at the western entrance of the Kiel Canal on the North Sea coast of Schleswig-Holstein.
-
B.
Nittendorf
Nittendorf is a municipality in the Upper Palatinate region of Bavaria, Germany, situated west of the city of Regensburg.
-
C.
Berkheim
Berkheim is a small municipality in the district of Biberach in the federal state of Baden-Württemberg in southern Germany.
-
D.
Wilhelminaoord
Wilhelminaoord is a village in the Dutch province of Drenthe, known for its origins as a 19th-century welfare colony and its inclusion in the UNESCO-listed Colonies of Benevolence.
-
E.
Wilhelmsdorf
Wilhelmsdorf is a village-level subdivision of the town of Usingen in the Hochtaunus district of Hesse, Germany.
- F. None of above. chosen
Provenance (5 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. |
| NEDg | Description generation | batch_69d183c71a44819092f556c8b1301fca |
completed | April 4, 2026, 9:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1842ba7088190bc663ede36b2d396 |
completed | April 4, 2026, 9:35 p.m. |
Created at: March 30, 2026, 8:11 p.m.