Triple
T11198135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UPC |
E264972
|
entity |
| Predicate | hasCampus |
P116
|
FINISHED |
| Object | Castelldefels |
E266955
|
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: Castelldefels | Statement: [UPC, hasCampus, Castelldefels]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Castelldefels Context triple: [UPC, hasCampus, Castelldefels]
-
A.
Castelldefels
chosen
Castelldefels is a coastal town near Barcelona in Catalonia, Spain, known for its long sandy beaches and residential character.
-
B.
Calella
Calella is a coastal town and popular tourist destination on the Mediterranean in the Maresme comarca of Catalonia, Spain.
-
C.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
D.
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.
-
E.
Castellolí
Castellolí is a small municipality in the Anoia comarca of Catalonia, Spain, known for its rural setting and proximity to the Montserrat mountain range.
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8c082fc8190866c574f698b59ef |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f03e00c819091ae5273aa10fe4b |
completed | May 3, 2026, 10:10 a.m. |
Created at: April 8, 2026, 9:29 p.m.