Triple
T365946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebanese cuisine |
E7958
|
entity |
| Predicate | typicalSpice |
P5084
|
FINISHED |
| Object | sumac |
—
|
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: sumac | Statement: [Lebanese cuisine, typicalSpice, sumac]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSpice Context triple: [Lebanese cuisine, typicalSpice, sumac]
-
A.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
-
B.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another entity.
-
C.
traditionalSweet
Indicates that something is a sweet food or dessert prepared according to long-established customs or cultural traditions.
-
D.
typicalFeatures
chosen
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
E.
typicalBlendPartner
Indicates that two entities are commonly or characteristically combined or mixed together as standard or usual partners.
- 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_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. |
Created at: Feb. 28, 2026, 1:08 p.m.