Triple
T365904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebanese cuisine |
E7958
|
entity |
| Predicate | usesIngredient |
P12771
|
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: [Lebanese cuisine, usesIngredient, olive oil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesIngredient Context triple: [Lebanese cuisine, usesIngredient, olive oil]
-
A.
hasMainIngredient
Indicates that one entity is the primary or most significant ingredient used to make another entity.
-
B.
isCookedBy
Indicates that something has been prepared or made ready for eating through cooking by a particular agent.
-
C.
foodCustom
Indicates a culturally specific practice, rule, or tradition related to the preparation, serving, or consumption of food.
-
D.
alsoServes
Indicates that an entity, in addition to its primary role or function, provides service or support to another specified entity or group.
-
E.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebe7d4d0819083daeb7686ae1914 |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95dbb208190b277fc5352a4ee84 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2eafc8da88190b4a05182f4384442 |
completed | Feb. 28, 2026, 1:17 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.