Triple
T18833636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamen |
E460596
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object | Lünen |
—
|
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: Lünen | Statement: [Kamen, hasNeighbouringMunicipality, Lünen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lünen Context triple: [Kamen, hasNeighbouringMunicipality, Lünen]
-
A.
Lünen
chosen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
B.
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.
-
C.
Nussloch
Nussloch is a small town in southwestern Germany, known in part for hosting the headquarters of medical technology company Leica Biosystems.
-
D.
Lüdinghausen
Lüdinghausen is a historic town in western Germany known for its medieval castles and picturesque setting in the Münsterland region.
-
E.
Lohfelden
Lohfelden is a German municipality known as a residential and industrial suburb near the city of Kassel in the state of Hesse.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a99b540c8190a29e5f9d56791d41 |
completed | April 20, 2026, 4:20 a.m. |
Created at: April 10, 2026, 11:56 a.m.