Triple
T8438337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mechernich |
E199285
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | Kommern-Süd |
E733417
|
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: Kommern-Süd | Statement: [Mechernich, hasDistrict, Kommern-Süd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kommern-Süd Context triple: [Mechernich, hasDistrict, Kommern-Süd]
-
A.
Kommern
chosen
Kommern is a historic district of the town of Mechernich in North Rhine-Westphalia, Germany, known for its well-preserved old town and open-air folk museum.
-
B.
Kandern
Kandern is a small town in southwestern Germany’s Baden-Württemberg state, known for its scenic Black Forest setting and traditional ceramics.
-
C.
Weidenau
Weidenau is a district of the city of Siegen in North Rhine-Westphalia, Germany.
-
D.
Geisenfeld
Geisenfeld is a small town in Bavaria, Germany, known as the birthplace of prominent early Nazi politician Gregor Strasser.
-
E.
Sennfeld
Sennfeld is a municipality in the Schweinfurt district of Bavaria, Germany, known for its traditional Franconian character and proximity to the city of Schweinfurt.
- 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_69ca8314cd6c8190a6b8c2a1096e18f3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe135657c81908ed8156fbfbef6ec |
completed | March 31, 2026, 2:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce399e8efc8190ad6fa8a6cf91797c |
completed | April 2, 2026, 9:40 a.m. |
Created at: March 30, 2026, 6:08 p.m.