Triple
T2127809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | baba ghanoush |
E46463
|
entity |
| Predicate | dietaryClassification |
P34157
|
FINISHED |
| Object | vegetarian |
—
|
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: vegetarian | Statement: [baba ghanoush, dietaryClassification, vegetarian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dietaryClassification Context triple: [baba ghanoush, dietaryClassification, vegetarian]
-
A.
nutritionType
Indicates the specific category or kind of nutritional characteristic or value associated with an entity.
-
B.
dietaryOptions
chosen
Indicates the types of diets or food-related preferences, restrictions, or choices that are applicable to or offered for an entity.
-
C.
characterizesDietAs
Indicates that one entity describes, defines, or assigns the type or nature of another entity’s diet.
-
D.
diet
Indicates that an entity regularly consumes a particular type or range of food as its primary source of nutrition.
-
E.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb75033881909b16659fc73945ef |
completed | March 7, 2026, 5:45 a.m. |
| PD | Predicate disambiguation | batch_69abb7bd86cc8190938ef06c1ed6d969 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:44 p.m.