Triple
T16108941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siegtal |
E390818
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Siegen |
—
|
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: Siegen | Statement: [Siegtal, hasSettlement, Siegen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegen Context triple: [Siegtal, hasSettlement, Siegen]
-
A.
Siegen
chosen
Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
-
B.
Siegen-Wittgenstein
Siegen-Wittgenstein is a rural district in the German state of North Rhine-Westphalia, known for its forested low mountain landscapes and the city of Siegen as its administrative center.
-
C.
Heppenheim
Heppenheim is a historic town in southwestern Germany, known for its picturesque old town, vineyards, and location on the Bergstraße at the edge of the Odenwald.
-
D.
Wuppertal
Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
-
E.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e20165aa9c81908c5358cca2b0d0fe |
completed | April 17, 2026, 9:46 a.m. |
Created at: April 10, 2026, 5 a.m.