Triple
T21555366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mödrath |
E531876
|
entity |
| Predicate | hasMunicipalAuthority |
P3379
|
FINISHED |
| Object | City of Kerpen |
—
|
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 Kerpen | Statement: [Mödrath, hasMunicipalAuthority, City of Kerpen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Kerpen Context triple: [Mödrath, hasMunicipalAuthority, City of 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.
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.
-
C.
Neunkirchen
Neunkirchen is an industrial town in Austria’s Lower Austria region, known historically for its manufacturing and metalworking industries.
-
D.
Kaldenkirchen
Kaldenkirchen is a town in western Germany near the Dutch border, known as a key cross-border transport point with direct access to major motorway routes.
-
E.
Erftstadt
Erftstadt is a town in the Rhein-Erft district of North Rhine-Westphalia, Germany, located southwest of Cologne and known for its mix of historic villages and suburban residential areas.
- 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_69e0c460232c81908de2c3819d17c00e |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eed2df48c88190894b6b08a5cb6390 |
completed | April 27, 2026, 3:07 a.m. |
Created at: April 16, 2026, 6:29 p.m.