Triple
T10769217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moianès |
E254030
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Moià |
E1046212
|
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: Moià | Statement: [Moianès, contains, Moià]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moià Context triple: [Moianès, contains, Moià]
-
A.
Moià
chosen
Moià is a historic town in central Catalonia, Spain, known for its rural surroundings and role as the administrative center of the Moianès comarca.
-
B.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
C.
Benicàssim
Benicàssim is a coastal town in eastern Spain best known for its Mediterranean beaches and the annual Festival Internacional de Benicàssim (FIB) music festival.
-
D.
Montmeló
Montmeló is a municipality in Catalonia, Spain, best known for hosting the Circuit de Barcelona-Catalunya, a major venue for Formula 1 and MotoGP races.
-
E.
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.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d732307fb88190ba1447f68523c58a |
completed | April 9, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f6baca4819080f85da5fe0c2aba |
completed | May 3, 2026, 5:01 p.m. |
Created at: April 8, 2026, 9:16 p.m.