Triple
T17242622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedrich Bayer |
E418538
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Elberfeld |
—
|
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: Elberfeld | Statement: [Friedrich Bayer, workLocation, Elberfeld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elberfeld Context triple: [Friedrich Bayer, workLocation, Elberfeld]
-
A.
Elberfeld
chosen
Elberfeld is a historic district of the German city of Wuppertal in North Rhine-Westphalia, known for its 19th-century architecture and early industrial development.
-
B.
Hennef
Hennef is a town in North Rhine-Westphalia, Germany, situated on the river Sieg near Bonn and known for its mix of residential areas, industry, and surrounding countryside.
-
C.
Oberhausen
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.
-
D.
Oberhausen
Oberhausen is a small Bavarian municipality in southern Germany, situated in the rural district of Weilheim-Schongau.
-
E.
Karsdorf
Karsdorf is a small municipality in the German state of Saxony-Anhalt, known for its location along the Unstrut River and its surrounding wine-growing and agricultural landscape.
- 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_69d886d8e96081909870bff6c3d0bf09 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e21003c81908c884a3c8712676a |
completed | April 19, 2026, 1:21 a.m. |
Created at: April 10, 2026, 5:39 a.m.