Triple
T20729508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kappel-Grafenhausen |
E509531
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Kappel (Ortsteil) |
—
|
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: Kappel (Ortsteil) | Statement: [Kappel-Grafenhausen, contains, Kappel (Ortsteil)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kappel (Ortsteil) Context triple: [Kappel-Grafenhausen, contains, Kappel (Ortsteil)]
-
A.
Kappel am Krappfeld
Kappel am Krappfeld is a small municipality in the Austrian state of Carinthia, known as the birthplace of painter Maria Lassnig.
-
B.
Kappel-Grafenhausen
chosen
Kappel-Grafenhausen is a municipality in the Ortenau district of Baden-Württemberg in southwestern Germany, situated near the Rhine River and the French border.
-
C.
Kappeln
Kappeln is a small town in northern Germany known for its picturesque harbor on the Schlei inlet and its traditional herring fishery.
-
D.
Cappeln
Cappeln is a rural municipality in Lower Saxony, Germany, situated within the Cloppenburg district.
-
E.
Waldkappel
Waldkappel is a small town and municipality in the Werra-Meißner district of northern Hesse, Germany, known for its rural setting and traditional half-timbered architecture.
- 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_69e0b4c589c08190834fb5d86d0efa2b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1eb5d44819082d9fa410e676d91 |
completed | April 21, 2026, 12:16 a.m. |
Created at: April 16, 2026, 12:30 p.m.