Triple
T851902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monaco |
E18404
|
entity |
| Predicate | subdivision |
P747
|
FINISHED |
| Object | Monaco-Ville |
E99129
|
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: Monaco-Ville | Statement: [Monaco, subdivision, Monaco-Ville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monaco-Ville Context triple: [Monaco, subdivision, Monaco-Ville]
-
A.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
B.
Fontvieille
chosen
Fontvieille is a modern district of Monaco built largely on reclaimed land, known for its marina, residential complexes, and commercial facilities.
-
C.
Mulhouse
Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
-
D.
Lancy
Lancy is a suburban municipality in western Switzerland that forms part of the urban area of Geneva.
-
E.
Antibes
Antibes is a historic resort town on the French Riviera known for its Mediterranean coastline, old town, and association with artists such as Pablo Picasso.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac22de288190913714d41e5a8e12 |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7cf53ee748190b5a08bd7a6d0614c |
completed | March 4, 2026, 6:21 a.m. |
Created at: March 1, 2026, 7:39 p.m.