Triple
T18364678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jake Canuso |
E440008
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Benidorm |
—
|
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: Benidorm | Statement: [Jake Canuso, notableWork, Benidorm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benidorm Context triple: [Jake Canuso, notableWork, Benidorm]
-
A.
Benidorm
chosen
Benidorm is a major Spanish Mediterranean resort city famous for its skyscraper-lined beaches, vibrant nightlife, and mass tourism.
-
B.
Denia
Denia is a coastal city on Spain’s Costa Blanca known for its historic castle, Mediterranean beaches, and vibrant port.
-
C.
Lloret de Mar
Lloret de Mar is a popular Mediterranean coastal resort town on Spain’s Costa Brava, known for its beaches, nightlife, and tourism.
-
D.
Marbella
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.
-
E.
Cala d'Or
Cala d'Or is a popular resort town on Mallorca’s southeastern coast, known for its sheltered coves, sandy beaches, and whitewashed, Ibizan-style architecture.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5174d31608190851a5bab6878c203 |
completed | April 19, 2026, 5:56 p.m. |
Created at: April 10, 2026, 10:38 a.m.