Triple
T18594475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rixheim |
E454456
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Lohne |
—
|
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: Lohne | Statement: [Rixheim, hasTwinTown, Lohne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lohne Context triple: [Rixheim, hasTwinTown, Lohne]
-
A.
Lohne
chosen
Lohne is a town in Lower Saxony, Germany, known for its industrial economy and location within the Vechta district.
-
B.
Stadtlohn
Stadtlohn is a small town in western Germany’s Münsterland region, near the Dutch border, known for its rural character and local industry.
-
C.
Werdohl
Werdohl is a town in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known for its metalworking industry and location in the hilly Sauerland region.
-
D.
Lahnau
Lahnau is a municipality in the Lahn-Dill district of the German state of Hesse, known for its location near the cities of Wetzlar and Gießen.
-
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_69d8d38ae7e081908a98df1251842402 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e545b8d76881909db1539c7150befb |
completed | April 19, 2026, 9:14 p.m. |
Created at: April 10, 2026, 11:44 a.m.