Triple
T3326396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aubrey Beardsley |
E69925
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object | Menton |
E194030
|
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: Menton | Statement: [Aubrey Beardsley, placeOfDeath, Menton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Menton Context triple: [Aubrey Beardsley, placeOfDeath, Menton]
-
A.
Menton
chosen
Menton is a picturesque coastal town on the French Riviera near the Italian border, known for its mild climate, gardens, and lemon festival.
-
B.
Fréjus
Fréjus is a historic town and seaside resort on the French Riviera in southeastern France, known for its Roman ruins and Mediterranean coastline.
-
C.
Le Cannet
Le Cannet is a commune in the Alpes-Maritimes department of southeastern France, located just north of Cannes on the French Riviera.
-
D.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
-
E.
Frontignan
Frontignan is a coastal commune in southern France known for its Muscat wine production and Mediterranean setting near Sète.
- 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_69ad85a1829881908942c14075644d0d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb16dc170819086a63e033e17d8b3 |
completed | March 8, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a7ce34c81908df0c30a41fd925c |
completed | March 12, 2026, 7:56 p.m. |
Created at: March 8, 2026, 3:12 p.m.