Triple
T17880416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Touch of Class |
E447067
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Marbella |
—
|
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: Marbella | Statement: [A Touch of Class, setting, Marbella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marbella Context triple: [A Touch of Class, setting, Marbella]
-
A.
Marbella
chosen
Marbella is a popular resort city on Spain’s Costa del Sol, known for its Mediterranean beaches, luxury marinas, upscale nightlife, and historic old town.
-
B.
Benalmádena
Benalmádena is a coastal resort town on Spain’s Costa del Sol, known for its beaches, marina, and tourist attractions near Málaga.
-
C.
Tarifa
Tarifa is a rural parish within Samborondón Canton in Ecuador’s Guayas Province.
-
D.
Tarifa
Tarifa is a coastal town in southern Spain known as the southernmost point of mainland Europe and a major destination for wind sports like kitesurfing and windsurfing.
-
E.
Málaga
Málaga is a historic port city on Spain’s Costa del Sol, renowned for its Mediterranean beaches, rich Andalusian culture, and as the birthplace of artist Pablo Picasso.
- 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c0e56bc819097649377b520de63 |
completed | April 19, 2026, 9:10 a.m. |
Created at: April 10, 2026, 10:18 a.m.