Triple
T6754642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limburg (Belgium) |
E154423
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Beringen |
E626116
|
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: Beringen | Statement: [Limburg (Belgium), contains, Beringen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beringen Context triple: [Limburg (Belgium), contains, Beringen]
-
A.
Beringen
chosen
Beringen is a city and municipality in the Belgian province of Limburg, known for its coal mining heritage and the be-MINE industrial heritage site.
-
B.
Bottendorf
Bottendorf is a locality in the German state of Thuringia that historically existed within the German Empire.
-
C.
Meerbeke
Meerbeke is a village in East Flanders, Belgium, best known for having long served as the traditional finish town of the Tour of Flanders cycling race.
-
D.
Merelbeke
Merelbeke is a municipality in East Flanders, Belgium, known in part for hosting Ghent University's Faculty of Veterinary Medicine.
-
E.
Izegem
Izegem is a town in the Belgian province of West Flanders, known historically for its shoe and brush industries.
- 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_69c6880fd5808190be684854081e27dd |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1f465388190858207ca4c48f18d |
completed | March 27, 2026, 6:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c968c9a1a48190b6ea5bb08ff745af |
completed | March 29, 2026, 6 p.m. |
Created at: March 27, 2026, 2:11 p.m.