Triple
T3781727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jiko – The Cooking Place |
E85432
|
entity |
| Predicate | wineProgramFocus |
P34683
|
FINISHED |
| Object | South African wines |
—
|
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: South African wines | Statement: [Jiko – The Cooking Place, wineProgramFocus, South African wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineProgramFocus Context triple: [Jiko – The Cooking Place, wineProgramFocus, South African wines]
-
A.
wineProgram
Indicates a relationship where an entity is part of, offered through, or associated with a specific wine-related program (such as a membership, curriculum, or organized initiative focused on wine).
-
B.
wineExport
Indicates a relationship where one entity exports wine to another entity or destination.
-
C.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
D.
hasProgramFocus
chosen
Indicates that an entity (such as a program or initiative) is oriented around or primarily concerned with a particular thematic area, topic, or objective.
-
E.
wineRegionStatus
Indicates the official classification or recognition status assigned to a wine-producing region.
- 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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee634c6ac819099653c660c286746 |
completed | March 9, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69aee3d3c92c819081d9d5c45ef37a5d |
completed | March 9, 2026, 3:14 p.m. |
Created at: March 9, 2026, 3:13 p.m.