Triple
T1854819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brest (Belarus) |
E41676
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Brest (France) |
E53827
|
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: Brest (France) | Statement: [Brest (Belarus), twinnedWith, Brest (France)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brest (France) Context triple: [Brest (Belarus), twinnedWith, Brest (France)]
-
A.
Brest
chosen
Brest is a major port city in northwestern France that serves as one of the country’s principal naval and maritime centers.
-
B.
Strasbourg
Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
-
C.
Nice, France
Nice, France is a major Mediterranean coastal city on the French Riviera known for its picturesque Promenade des Anglais, vibrant arts scene, and historic old town.
-
D.
Rennes
Rennes is the capital city of France’s Brittany region, known for its historic medieval center, vibrant student population, and role as a major cultural and economic hub in western France.
-
E.
Valenciennes
Valenciennes is a historic industrial city in northern France near the Belgian border, known for its former coal and steel industries and its rich artistic and architectural heritage.
- 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_69a8864a83848190a4ec02721306c511 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb07d48c48190bcd34d6093ff5e78 |
completed | March 7, 2026, 4:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae516ffd088190ae2c730e1caff8f6 |
completed | March 9, 2026, 4:49 a.m. |
Created at: March 4, 2026, 7:33 p.m.