Triple
T9481411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maubeuge |
E228647
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Blankenberge |
E773652
|
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: Blankenberge | Statement: [Maubeuge, twinnedWith, Blankenberge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blankenberge Context triple: [Maubeuge, twinnedWith, Blankenberge]
-
A.
Blankenberge
chosen
Blankenberge is a Belgian coastal town on the North Sea known for its sandy beaches, seaside promenade, and tourism.
-
B.
Veurne
Veurne is a historic town in western Belgium known for its well-preserved medieval center and Flemish Renaissance architecture.
-
C.
Lieshout
Lieshout is a village in the Dutch province of North Brabant, known for its rural character and the Bavaria brewery.
-
D.
Merelbeke
Merelbeke is a municipality in East Flanders, Belgium, known in part for hosting Ghent University's Faculty of Veterinary Medicine.
-
E.
Zeewolde
Zeewolde is a Dutch municipality and village known for its modern planned layout and location on reclaimed land in the province of Flevoland.
- 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_69ca84730a5081908de282651019bf2f |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd8018645c8190823d82a93635b345 |
completed | April 1, 2026, 8:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12cfebb6c8190af3296c7bfd8b3e4 |
completed | April 4, 2026, 3:23 p.m. |
Created at: March 30, 2026, 7:54 p.m.