Triple
T23336841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reus Airport |
E591613
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Salou |
—
|
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: Salou | Statement: [Reus Airport, locatedNear, Salou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salou Context triple: [Reus Airport, locatedNear, Salou]
-
A.
Salou
chosen
Salou is a popular coastal resort town on Spain’s Costa Daurada, known for its beaches, tourism, and proximity to the PortAventura World theme park.
-
B.
Premià de Mar
Premià de Mar is a coastal town and municipality in the comarca of Maresme in Catalonia, northeastern Spain, known for its Mediterranean beaches and proximity to Barcelona.
-
C.
Deià
Deià is a picturesque coastal village on the Spanish island of Mallorca, famed for its dramatic mountain-and-sea scenery and its long association with artists and writers.
-
D.
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.
-
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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197f1e0588190bf073b92be0bf9e4 |
completed | April 29, 2026, 5:32 a.m. |
Created at: April 17, 2026, 5:17 p.m.