Triple
T3874819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnsberg region |
E92473
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | city of Siegen |
E289225
|
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: city of Siegen | Statement: [Arnsberg region, contains, city of Siegen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: city of Siegen Context triple: [Arnsberg region, contains, city of 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.
Solingen
Solingen is a city in western Germany renowned for its centuries-old blade-making tradition and production of high-quality knives and swords.
-
D.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
-
E.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
- 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec59bea08190b1e193f34944a2ee |
completed | March 9, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b576852d0c819083d377bab6799ec7 |
completed | March 14, 2026, 2:53 p.m. |
Created at: March 9, 2026, 3:20 p.m.