Triple
T9288230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kir |
E223450
|
entity |
| Predicate | drinkingCulture |
P4038
|
FINISHED |
| Object | French cuisine |
—
|
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: French cuisine | Statement: [Kir, drinkingCulture, French cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drinkingCulture Context triple: [Kir, drinkingCulture, French cuisine]
-
A.
drinkingHabit
Indicates an entity’s typical pattern or frequency of consuming alcoholic or other beverages.
-
B.
traditionalDrink
chosen
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
C.
nationalWineTradition
Indicates that a country or region has an established cultural and historical practice of producing, consuming, and valuing wine.
-
D.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
-
E.
madeWithAlcohol
Indicates that something is created, prepared, or produced using alcohol as an ingredient or component.
- 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_69ca8422ddf881908a3f8f876c9f53aa |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd0862b34c819097cb7c1777313925 |
completed | April 1, 2026, 11:58 a.m. |
| PD | Predicate disambiguation | batch_69cc7a5aeb748190afb89c6bbd2a6d6f |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:35 p.m.