Triple
T7160805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sambre |
E166937
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Namur |
E107798
|
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: Namur | Statement: [Sambre, flowsThrough, Namur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Namur Context triple: [Sambre, flowsThrough, Namur]
-
A.
Namur
chosen
Namur is a historic Belgian city and the capital of Wallonia, located at the confluence of the Meuse and Sambre rivers.
-
B.
Nivelles
Nivelles is a historic town in present-day Belgium known for its medieval architecture, including the Romanesque Collegiate Church of Saint Gertrude.
-
C.
Durbuy
Durbuy is a small, picturesque town in the Belgian Ardennes often promoted as one of the “smallest cities in the world,” known for its medieval architecture and tourism.
-
D.
Binche
Binche is a historic town in the Walloon region of Belgium, renowned for its well-preserved medieval architecture and its UNESCO-recognized Carnival of Binche.
-
E.
Liège
Liège is a major city in eastern Belgium known for its industrial heritage, vibrant cultural scene, and position along the Meuse River.
- 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_69c68887a5cc8190bec0ea96227164f7 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e82ce770819081dccf7ffd50c2ab |
completed | March 27, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9a9ca03ec8190859d9728fef39d24 |
completed | March 29, 2026, 10:38 p.m. |
Created at: March 27, 2026, 2:47 p.m.