Triple
T3874815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnsberg region |
E92473
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | city of Hagen |
E291575
|
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 Hagen | Statement: [Arnsberg region, contains, city of Hagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: city of Hagen Context triple: [Arnsberg region, contains, city of Hagen]
-
A.
Hagen
chosen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
B.
Hodenhagen
Hodenhagen is a small municipality in Lower Saxony, Germany, known for its rural setting along the Aller River and proximity to attractions like the Serengeti Park safari zoo.
-
C.
Iserlohn
Iserlohn is a city in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known historically for its role in World War II and its metalworking and industrial heritage.
-
D.
Hemfurth
Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
-
E.
Siegen
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.
- 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_69b5124cdf588190b3b83ee8fb29450a |
completed | March 14, 2026, 7:46 a.m. |
Created at: March 9, 2026, 3:20 p.m.