Triple
T10804509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alt Penedès |
E254928
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Vilafranca del Penedès |
E305686
|
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: Vilafranca del Penedès | Statement: [Alt Penedès, capital, Vilafranca del Penedès]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vilafranca del Penedès Context triple: [Alt Penedès, capital, Vilafranca del Penedès]
-
A.
Vilafranca del Penedès
chosen
Vilafranca del Penedès is a historic town in Catalonia, Spain, known as a traditional wine-producing center in the Penedès region.
-
B.
Benicàssim
Benicàssim is a coastal town in eastern Spain best known for its Mediterranean beaches and the annual Festival Internacional de Benicàssim (FIB) music festival.
-
C.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
D.
Vilanova del Vallès
Vilanova del Vallès is a municipality in the province of Barcelona, Catalonia, Spain, known for its semi-rural character within the Vallès Oriental comarca.
-
E.
Calella
Calella is a coastal town and popular tourist destination on the Mediterranean in the Maresme comarca of Catalonia, Spain.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73370e7388190885b104fc883456e |
completed | April 9, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c0cf61108190a15ab76454fc0d75 |
completed | May 3, 2026, 9:40 p.m. |
Created at: April 8, 2026, 9:18 p.m.