Triple
T15499917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wahnbach |
E378923
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Neunkirchen-Seelscheid |
E948621
|
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: Neunkirchen-Seelscheid | Statement: [Wahnbach, flowsThrough, Neunkirchen-Seelscheid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neunkirchen-Seelscheid Context triple: [Wahnbach, flowsThrough, Neunkirchen-Seelscheid]
-
A.
Neunkirchen-Seelscheid
chosen
Neunkirchen-Seelscheid is a municipality in the German state of North Rhine-Westphalia, situated in the rural area east of Bonn.
-
B.
Neunkirchen
Neunkirchen is an industrial town in Austria’s Lower Austria region, known historically for its manufacturing and metalworking industries.
-
C.
Neunkirchen
Neunkirchen is a town in southwestern Germany known as one of the major urban centers and former industrial hubs of the state of Saarland.
-
D.
Lüdenscheid
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.
-
E.
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.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcb4e8c81908e4ab463e3ae252b |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d4a9bf88190b7c6b4874abe165f |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 3:54 a.m.