Triple
T7506564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münsterland |
E177404
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Coesfeld |
E205349
|
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: Coesfeld | Statement: [Münsterland, hasCity, Coesfeld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coesfeld Context triple: [Münsterland, hasCity, Coesfeld]
-
A.
Coesfeld
chosen
Coesfeld is a town in the Münster region of North Rhine-Westphalia, Germany, known as a local administrative and commercial center.
-
B.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
-
C.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
D.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
-
E.
Fritzlar
Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b76a288190bb3608a5e3bfa212 |
completed | March 27, 2026, 9:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce88013b58819098bb60188fee1465 |
completed | April 2, 2026, 3:15 p.m. |
Created at: March 27, 2026, 3:45 p.m.