Triple
T15610917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eschweiler |
E375285
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object | Würselen |
E850465
|
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: Würselen | Statement: [Eschweiler, hasNeighbouringMunicipality, Würselen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Würselen Context triple: [Eschweiler, hasNeighbouringMunicipality, Würselen]
-
A.
Würselen
chosen
Würselen is a town in western Germany’s state of North Rhine-Westphalia, located near the city of Aachen and known historically for its mining and industrial heritage.
-
B.
Raunheim
Raunheim is a town in the German state of Hesse, located near Frankfurt am Main and known for its proximity to major transportation routes and Frankfurt Airport.
-
C.
Herzogenrath
Herzogenrath is a town in western Germany near the Dutch border, known for its cross-border cooperation with the neighboring Dutch town of Kerkrade.
-
D.
Waldbröl
Waldbröl is a small town in North Rhine-Westphalia, Germany, known for its rural setting in the Bergisches Land region.
-
E.
Rüttenscheid
Rüttenscheid is a lively, upscale district of Essen, Germany, known for its bustling shopping streets, restaurants, and cultural venues.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e8148a0819087d6d69cc84487ca |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0025e9b00c81908cb5f305c894363f |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 4:13 a.m.