Triple
T21624129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare de Beaulieu-sur-Mer |
E533652
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Monaco |
—
|
NE NERFINISHED |
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: Monaco | Statement: [Gare de Beaulieu-sur-Mer, near, Monaco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monaco Context triple: [Gare de Beaulieu-sur-Mer, near, Monaco]
-
A.
Monaco
Monaco was the original name of Crypto.com, a cryptocurrency and financial services platform known for its crypto-backed payment cards and trading app.
-
B.
Monaco
chosen
Monaco is a small sovereign city-state on the French Riviera known for its wealth, luxury tourism, and status as a major tax haven and gambling hub.
-
C.
AS Monaco
AS Monaco is a prominent football club based in the Principality of Monaco, known for competing in France's top division and developing numerous world-class players.
-
D.
Andorra
Andorra is a small, landlocked principality in the eastern Pyrenees between France and Spain, known for its mountainous terrain, tourism, and status as a tax haven.
-
E.
Monégasque
Monégasque is the traditional Ligurian-based Romance language of Monaco, historically spoken by the native Monégasque population.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c464fba881908d0ff2ac80511ce1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef521125f481909ccfc95d884976e2 |
completed | April 27, 2026, 12:09 p.m. |
Created at: April 16, 2026, 6:34 p.m.