Triple
T9290443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Castell de Sant Ferran |
E223502
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Alt Empordà |
E231220
|
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: Alt Empordà | Statement: [Castell de Sant Ferran, locatedIn, Alt Empordà]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alt Empordà Context triple: [Castell de Sant Ferran, locatedIn, Alt Empordà]
-
A.
Empordà
chosen
Empordà is a historic coastal region in northeastern Catalonia, Spain, known for its medieval villages, Mediterranean landscapes, and strong cultural ties to the artist Salvador Dalí.
-
B.
Segarra
Segarra is a historical inland comarca in Catalonia, Spain, known for its rolling cereal plains, medieval castles, and the town of Cervera as its capital.
-
C.
Berguedà
Berguedà is a mountainous comarca in central Catalonia, Spain, known for its Pyrenean landscapes, rural villages, and natural parks.
-
D.
Conca de Barberà
Conca de Barberà is a comarca (county) in Catalonia, Spain, known for its medieval heritage, wine production, and the presence of the UNESCO-listed Poblet Monastery.
-
E.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
- 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_69ca8422ddf881908a3f8f876c9f53aa |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd0865a7108190b807afd259980db2 |
completed | April 1, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d107800f6881909befc391a2b4f623 |
completed | April 4, 2026, 12:43 p.m. |
Created at: March 30, 2026, 7:35 p.m.