Triple
T12235362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hagen |
E291575
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | South Westphalia |
E395575
|
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: South Westphalia | Statement: [Hagen, region, South Westphalia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: South Westphalia Context triple: [Hagen, region, South Westphalia]
-
A.
South Westphalia
chosen
South Westphalia is a region in western Germany known for its mixed industrial and rural character, encompassing parts of North Rhine-Westphalia including the Arnsberg area.
-
B.
Westfalen
Westfalen is a historical region in northwestern Germany, now largely part of the state of North Rhine-Westphalia, known for its distinct cultural identity and medieval heritage.
-
C.
Münsterland
Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
-
D.
Weserbergland
Weserbergland is a hilly, forested region in central Germany known for its picturesque landscapes along the Weser River and numerous historic towns.
-
E.
North Rhine-Westphalia
North Rhine-Westphalia is Germany’s most populous federal state, known for its major industrial regions, cultural hubs like Cologne and Düsseldorf, and numerous universities and research institutions.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cb2892c81909a97b3ad6ec2c21b |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6555903b08190a8c70bf7eda47dff |
completed | May 2, 2026, 7:49 p.m. |
Created at: April 8, 2026, 9:51 p.m.