Triple
T23934401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campari |
E602589
|
entity |
| Predicate | recipe |
P154412
|
FINISHED |
| Object | secret formula |
—
|
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: secret formula | Statement: [Campari, recipe, secret formula]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recipe Context triple: [Campari, recipe, secret formula]
-
A.
traditionalDish
Indicates that the object is a dish customarily prepared, eaten, or recognized within the subject’s cultural or regional tradition.
-
B.
cuisine
Indicates the type or style of food traditionally associated with or served by an entity (such as a restaurant or region).
-
C.
intendedMeal
Indicates that one entity is the meal that another entity plans or expects to eat.
-
D.
usesRecipeOf
Indicates that one entity prepares or creates something by following the recipe or formula originally defined or used by another entity.
-
E.
chef
Indicates that one entity serves as the cook or culinary professional responsible for preparing food for another entity or context.
- F. None of above. chosen
Provenance (4 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_69e2953cf6e081909b8e25a10a52dddc |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1cf9da9c4819085880c6ad521d7ed |
completed | April 29, 2026, 9:30 a.m. |
| PD | Predicate disambiguation | batch_69f16151ebdc819086e9e1d7cc1f4f3c |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f16e348b548190b76e50f9b611f76d |
completed | April 29, 2026, 2:34 a.m. |
Created at: April 17, 2026, 8:59 p.m.