Triple
T12463379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sillery |
E297856
|
entity |
| Predicate | secondaryGrapeColor |
P39767
|
FINISHED |
| Object | white grapes (Chardonnay) |
—
|
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: white grapes (Chardonnay) | Statement: [Sillery, secondaryGrapeColor, white grapes (Chardonnay)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryGrapeColor Context triple: [Sillery, secondaryGrapeColor, white grapes (Chardonnay)]
-
A.
secondaryGrape
chosen
Indicates that one grape variety serves as a secondary or supporting component in a wine blend relative to the primary grape.
-
B.
secondaryColour
Indicates that one entity serves as a secondary or accent color in relation to another entity’s primary color.
-
C.
secondaryWine
Indicates a relationship where one wine is designated as a secondary or supporting wine in relation to a primary wine.
-
D.
secondaryBranchColor
Indicates the color assigned to a secondary or subordinate branch in relation to a primary branch.
-
E.
grapeColorForReds
Indicates that the predicate specifies the typical color of grapes used to produce red wines.
- 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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e626dbc8190ac7dcdb542ba9b0c |
completed | April 10, 2026, 7:24 p.m. |
| PD | Predicate disambiguation | batch_69d94d3f701c81909dd0e00251ac8553 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:56 p.m.