Triple
T10644894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berguedà |
E250811
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Gironella |
E341436
|
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: Gironella | Statement: [Berguedà, contains, Gironella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gironella Context triple: [Berguedà, contains, Gironella]
-
A.
Gironella
chosen
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
B.
Garriga
Garriga is a Spanish surname most notably borne by Ignacio Garriga, a contemporary Spanish politician.
-
C.
Noguera Pallaresa
Noguera Pallaresa is a river in the Catalan Pyrenees of northeastern Spain, renowned for its whitewater rafting and kayaking.
-
D.
Corberó
Corberó is a Spanish surname most notably associated with actress Úrsula Corberó, known internationally for her role in the series "Money Heist" (La Casa de Papel).
-
E.
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.
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfd04ca88190ac4fffd13c1f33a8 |
completed | April 8, 2026, 11:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e154679bb88190b2fffeea74d1fc50 |
completed | April 16, 2026, 9:28 p.m. |
Created at: April 8, 2026, 9:05 p.m.