Triple
T18492602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bonarda |
E451853
|
entity |
| Predicate | marketRoleInArgentina |
P131879
|
FINISHED |
| Object | one of the most planted red varieties |
—
|
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: one of the most planted red varieties | Statement: [Bonarda, marketRoleInArgentina, one of the most planted red varieties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marketRoleInArgentina Context triple: [Bonarda, marketRoleInArgentina, one of the most planted red varieties]
-
A.
roleInPeru
Indicates that an entity holds or has held an official or notable role, position, or function within the country of Peru.
-
B.
roleInBrazil
Indicates that an entity holds or held a specific role, position, or function within the context of Brazil.
-
C.
argentineSectorPartOf
Indicates that one entity is a sector or subdivision that forms a component part of a larger Argentine administrative or geographic unit.
-
D.
unitArgentina
Indicates a relationship where an entity is associated with, belongs to, or is characterized as a unit related to Argentina.
-
E.
commandStructureArgentine
Indicates a command or leadership relationship within the Argentine command structure.
- F. None of above. chosen
Provenance (4 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e532be5e988190aae93a66f6e5f857 |
completed | April 19, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69e469d671088190b619de96ea6f92ab |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2aa72c8190a40854a7a52081e2 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:35 a.m.