Triple
T28553770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tunisian cuisine |
E722950
|
entity |
| Predicate | cookingFat |
P158046
|
FINISHED |
| Object | olive oil |
—
|
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: olive oil | Statement: [Tunisian cuisine, cookingFat, olive oil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cookingFat Context triple: [Tunisian cuisine, cookingFat, olive oil]
-
A.
typicalFatContent
Indicates the usual or characteristic amount of fat contained in something, such as a food or product.
-
B.
fattyAcid
Indicates a relationship where one entity is a fatty acid component, derivative, or participant in a process involving another entity.
-
C.
fatColor
Indicates the color characteristic associated with an entity’s fat or fatty tissue.
-
D.
cookingFuel
chosen
Indicates that one entity serves as the fuel or energy source used by another entity for cooking activities.
-
E.
fryCook
Indicates that one entity works as a cook who prepares food by frying, typically in a restaurant or similar setting, for another entity (such as an employer or establishment).
- 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_69f01a60204481909af1bb76247b8221 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f6504de65c81908ba7a633af156609 |
completed | May 2, 2026, 7:28 p.m. |
| PD | Predicate disambiguation | batch_69f64cb0d8008190912e1430cfaf92aa |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 3:44 a.m.