Triple
T24909113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viura |
E623795
|
entity |
| Predicate | synonymUsedInRegion |
P46176
|
FINISHED |
| Object | Macabeo in Catalonia |
—
|
LITERAL 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: Macabeo in Catalonia | Statement: [Viura, synonymUsedInRegion, Macabeo in Catalonia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: synonymUsedInRegion Context triple: [Viura, synonymUsedInRegion, Macabeo in Catalonia]
-
A.
synonym
Indicates that two terms have the same or nearly the same meaning in a given context.
-
B.
linguisticallyRelatedTo
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
C.
cognateOf
Indicates that two linguistic forms share a common historical origin, typically descending from the same ancestral word.
-
D.
termAlsoUsedFor
chosen
Indicates that one term is also used to refer to the same or closely related concept as another term.
-
E.
equivalentEpithetLanguage
Indicates that two epithets are expressed in different languages but convey the same meaning or designation.
- F. None of above.
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_69e2fac797cc8190b30d77f4121099ac |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f4236db55c8190a1c55a0db86562d8 |
completed | May 1, 2026, 3:52 a.m. |
| PD | Predicate disambiguation | batch_69f4210130d08190ae30b7943f7a0bbc |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:27 a.m.