Triple
T348096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabernet Franc |
E6983
|
entity |
| Predicate | roleInBordeaux |
P12668
|
FINISHED |
| Object | one of the major black grape 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 major black grape varieties | Statement: [Cabernet Franc, roleInBordeaux, one of the major black grape varieties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInBordeaux Context triple: [Cabernet Franc, roleInBordeaux, one of the major black grape varieties]
-
A.
hasCityRole
Indicates that an entity holds or is assigned a specific role, function, or status within a particular city.
-
B.
urbanRole
Indicates the function, status, or role that an entity holds within an urban or city context.
-
C.
roleInMedina
Indicates the specific function, position, or responsibility an entity holds within the context of Medina.
-
D.
nameInFrench
Indicates that an entity is known or referred to by a specific name expressed in the French language.
-
E.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
- 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb1c1c908190b3a01de893207ed1 |
completed | Feb. 28, 2026, 1:18 p.m. |
| PD | Predicate disambiguation | batch_69a2e95451a4819090f4e4fb9b21a493 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2eae0bd7081908197bbf5c55fe647 |
completed | Feb. 28, 2026, 1:17 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.