Triple
T14815799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francs Côtes de Bordeaux AOC |
E348309
|
entity |
| Predicate | typicalServingSuggestion |
P49932
|
FINISHED |
| Object | pairs with red meats |
—
|
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: pairs with red meats | Statement: [Francs Côtes de Bordeaux AOC, typicalServingSuggestion, pairs with red meats]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalServingSuggestion Context triple: [Francs Côtes de Bordeaux AOC, typicalServingSuggestion, pairs with red meats]
-
A.
isTypicallyServedFor
Indicates that one item is most commonly or customarily served as a meal or course for the other (e.g., a dish typically served for breakfast, lunch, or dinner).
-
B.
traditionallyServed
Indicates that one entity is customarily or conventionally presented, offered, or consumed together with another entity.
-
C.
isTypicallyGarnishedWith
Indicates that one item is commonly used as a garnish or decorative finishing element for another.
-
D.
servingStyle
Indicates how something (typically food or drink) is presented or offered for consumption or use.
-
E.
servingSuggestionRed
chosen
Indicates that something is recommended or suggested to be served together with a red wine.
- 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_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decfe2c1ec81908b3dff7a5d0e85d0 |
completed | April 14, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69de8c0ef8a4819092d84478b1f56db1 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:49 a.m.