Triple
T13314583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ponent |
E317156
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Pla d'Urgell |
E614163
|
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: Pla d'Urgell | Statement: [Ponent, contains, Pla d'Urgell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pla d'Urgell Context triple: [Ponent, contains, Pla d'Urgell]
-
A.
Pla d'Urgell
chosen
Pla d'Urgell is a comarca (county) in the inland plains of Catalonia, Spain, known for its irrigated agriculture and small rural towns.
-
B.
Quart de Poblet
Quart de Poblet is a municipality in the province of Valencia, Spain, known for its proximity to the city of Valencia and its role within the metropolitan area.
-
C.
Sant Sadurní d’Anoia
Sant Sadurní d’Anoia is a Catalan town renowned as the main center of cava (sparkling wine) production in Spain.
-
D.
Pedralbes
Pedralbes is an affluent residential neighborhood in Barcelona known for its upscale homes, green spaces, and prestigious educational institutions.
-
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_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_69f79d360c60819086a8168bdc092e1c |
completed | May 3, 2026, 7:08 p.m. |
Created at: April 9, 2026, 9:29 p.m.