Triple
T20345255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gasometer Oberhausen |
E495848
|
entity |
| Predicate | ownedBy |
P347
|
FINISHED |
| Object | City of Oberhausen |
—
|
NE NERFINISHED |
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: City of Oberhausen | Statement: [Gasometer Oberhausen, ownedBy, City of Oberhausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Oberhausen Context triple: [Gasometer Oberhausen, ownedBy, City of Oberhausen]
-
A.
Oberhausen
chosen
Oberhausen is an industrial city in Germany’s Ruhr region, historically known for its coal and steel production and heavily affected by World War II bombing.
-
B.
Oberhausen
Oberhausen is a small Bavarian municipality in southern Germany, situated in the rural district of Weilheim-Schongau.
-
C.
Oberhausen an der Nahe
Oberhausen an der Nahe is a small winegrowing village in Germany’s Nahe region, known for its high-quality Riesling vineyards along the Nahe River.
-
D.
City of Essen
The City of Essen is a major urban center in Germany’s Ruhr area, historically significant as a medieval ecclesiastical seat and later as an important industrial and coal-mining hub.
-
E.
City of Wesel
The City of Wesel is a historic German town on the Lower Rhine that became an important Reformation and trading center in the early modern period.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67838744481909069b76b25dd4bb9 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:24 a.m.