Triple
T11619492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sant Cugat del Vallès |
E275599
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Terrassa |
E188972
|
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: Terrassa | Statement: [Sant Cugat del Vallès, hasNeighbour, Terrassa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terrassa Context triple: [Sant Cugat del Vallès, hasNeighbour, Terrassa]
-
A.
Terrassa
chosen
Terrassa is a city in Catalonia, Spain, known as part of the Barcelona metropolitan area and for its industrial heritage and modernist architecture.
-
B.
Canigó
Canigó is a prominent mountain in the eastern Pyrenees of southern France, culturally significant to Catalan identity and often celebrated in regional literature and tradition.
-
C.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
D.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
E.
Valldemossa
Valldemossa is a picturesque mountain village on the Spanish island of Mallorca, renowned for its historic Carthusian monastery and scenic stone streets.
- 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_69d6aaf84b548190ac072e4fb89ae18f |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a047758081908191c1d564409d9a |
completed | April 10, 2026, 7:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee874fe11c81908b9d3a996bcc51d2 |
completed | April 26, 2026, 9:44 p.m. |
Created at: April 8, 2026, 9:38 p.m.