Triple
T10804668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lluís Domènech i Montaner |
E254929
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Canet de Mar |
E878555
|
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: Canet de Mar | Statement: [Lluís Domènech i Montaner, residence, Canet de Mar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Canet de Mar Context triple: [Lluís Domènech i Montaner, residence, Canet de Mar]
-
A.
Canet de Mar
chosen
Canet de Mar is a coastal town in Catalonia, Spain, known for its modernist architecture and Mediterranean beaches.
-
B.
Arenys de Mar
Arenys de Mar is a coastal town and municipality in the Maresme comarca of Catalonia, Spain, known for its fishing port and Mediterranean beaches.
-
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.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
E.
Vilassar de Mar
Vilassar de Mar is a coastal town and municipality on the Mediterranean in the Maresme comarca of Catalonia, Spain, known for its beaches and residential character.
- 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_69e373d238b88190a6f44fecfb64410c |
completed | April 18, 2026, 12:06 p.m. |
Created at: April 8, 2026, 9:18 p.m.