Triple
T16424471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Märkischer Kreis |
E398905
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Lüdenscheid |
—
|
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: Lüdenscheid | Statement: [Märkischer Kreis, capital, Lüdenscheid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lüdenscheid Context triple: [Märkischer Kreis, capital, Lüdenscheid]
-
A.
Lüdenscheid
chosen
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
-
B.
Lüdinghausen
Lüdinghausen is a historic town in western Germany known for its medieval castles and picturesque setting in the Münsterland region.
-
C.
Burscheid
Burscheid is a small town in North Rhine-Westphalia, Germany, known for its location in the hilly Bergisches Land region and its mix of rural character and local industry.
-
D.
Schwelm
Schwelm is a small town in North Rhine-Westphalia, Germany, known as the administrative seat of the Ennepe-Ruhr district.
-
E.
Rüttenscheid
Rüttenscheid is a lively, upscale district of Essen, Germany, known for its bustling shopping streets, restaurants, and cultural venues.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328f9da9081908dadbdac4b2d38ec |
completed | April 18, 2026, 6:47 a.m. |
Created at: April 10, 2026, 5:09 a.m.