Triple
T3874804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnsberg region |
E92473
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | 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: Siegen | Statement: [Arnsberg region, contains, Siegen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegen Context triple: [Arnsberg region, contains, 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.
Wuppertal
Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
-
C.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
-
D.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
E.
Siegburg
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
- 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_69b562821c3c81909805cb877288405b |
completed | March 14, 2026, 1:28 p.m. |
Created at: March 9, 2026, 3:20 p.m.