Triple
T13314585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ponent |
E317156
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Segarra |
E614164
|
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: Segarra | Statement: [Ponent, contains, Segarra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Segarra Context triple: [Ponent, contains, Segarra]
-
A.
Segarra
chosen
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.
-
B.
Gandria
Gandria is a picturesque lakeside village in southern Switzerland known for its historic stone houses, narrow alleyways, and scenic setting on the shores of Lake Lugano.
-
C.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
D.
Gandesa
Gandesa is a historic town in Catalonia, Spain, known for its wine production and role in the Battle of the Ebro during the Spanish Civil War.
-
E.
Figaró-Montmany
Figaró-Montmany is a small municipality in the province of Barcelona, Catalonia, Spain, situated in a mountainous area near the Montseny Natural Park.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990f8a86481909ea2942c63037b77 |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a83125d481908fe02cf85651a7bb |
completed | May 3, 2026, 7:55 p.m. |
Created at: April 9, 2026, 9:29 p.m.