Triple
T9149312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KFC Uerdingen 05 |
E219540
|
entity |
| Predicate | homeCity |
P263
|
FINISHED |
| Object | Krefeld-Uerdingen |
E268210
|
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: Krefeld-Uerdingen | Statement: [KFC Uerdingen 05, homeCity, Krefeld-Uerdingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krefeld-Uerdingen Context triple: [KFC Uerdingen 05, homeCity, Krefeld-Uerdingen]
-
A.
Krefeld
Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
-
B.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
C.
Uerdingen
chosen
Uerdingen is a district of the German city of Krefeld, known historically for its chemical industry and location along the Rhine River.
-
D.
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.
-
E.
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.
- 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_69ca83e121dc81909912bd66953081c5 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96908d88190a1d12517f6d1aece |
completed | April 1, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d04833858081909b616325f0e6f787 |
completed | April 3, 2026, 11:07 p.m. |
Created at: March 30, 2026, 7:20 p.m.