Triple
T1617109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waterloo, Ontario |
E34744
|
entity |
| Predicate | hasSisterCity |
P919
|
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: [Waterloo, Ontario, hasSisterCity, Coesfeld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coesfeld Context triple: [Waterloo, Ontario, hasSisterCity, 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.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
-
C.
Nienburg
Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
-
D.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
E.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a909abbec081908f95547471530ad5 |
completed | March 5, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae303cdce081909b66ec04de43cf53 |
completed | March 9, 2026, 2:28 a.m. |
Created at: March 4, 2026, 7:28 p.m.