Triple
T3724704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Le Pin |
E81720
|
entity |
| Predicate | typicalBlend |
P44611
|
FINISHED |
| Object | predominantly Merlot with small proportion of Cabernet Franc |
—
|
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: predominantly Merlot with small proportion of Cabernet Franc | Statement: [Château Le Pin, typicalBlend, predominantly Merlot with small proportion of Cabernet Franc]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBlend Context triple: [Château Le Pin, typicalBlend, predominantly Merlot with small proportion of Cabernet Franc]
-
A.
typicalBlendStyle
Indicates the usual or characteristic way in which two or more elements are combined or mixed together.
-
B.
typicalBlendPartner
Indicates that two entities are commonly or characteristically combined or mixed together as standard or usual partners.
-
C.
typicalBlendShare
chosen
Indicates the usual proportion or percentage that one component contributes to a blend relative to the other components.
-
D.
typicalColorDescription
Indicates the usual or characteristic color associated with an entity.
-
E.
typicalTexture
Indicates the usual or characteristic surface feel or consistency that is commonly associated with an entity.
- 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_69ad8b1b7ef081908d2d381bbf54985a |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcaf54af881908bd8d520595de061 |
completed | March 8, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69adc0452f5081909c79e114a86cce8c |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.