Triple
T12033804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frechen |
E286481
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object | Kerpen |
E153193
|
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: Kerpen | Statement: [Frechen, hasNeighbouringMunicipality, Kerpen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kerpen Context triple: [Frechen, hasNeighbouringMunicipality, Kerpen]
-
A.
Kerpen
chosen
Kerpen is a town in North Rhine-Westphalia, Germany, known as the birthplace of Formula 1 champion Michael Schumacher and for its proximity to Cologne.
-
B.
Mechernich
Mechernich is a small town in the Eifel region of North Rhine-Westphalia, Germany, known for its rural landscape and cultural landmarks such as the Bruder Klaus Field Chapel.
-
C.
Neunkirchen
Neunkirchen is an industrial town in Austria’s Lower Austria region, known historically for its manufacturing and metalworking industries.
-
D.
Neunkirchen
Neunkirchen is a town in southwestern Germany known as one of the major urban centers and former industrial hubs of the state of Saarland.
-
E.
Bottendorf
Bottendorf is a locality in the German state of Thuringia that historically existed within the German Empire.
- 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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9040724ec8190808f334013ddc6d6 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49d6ec4b8819093ff50254a851444 |
completed | May 1, 2026, 12:32 p.m. |
Created at: April 8, 2026, 9:47 p.m.