Triple
T11548971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rumex |
E273841
|
entity |
| Predicate | culinaryRegion |
P45120
|
FINISHED |
| Object | used traditionally in European cuisines |
—
|
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: used traditionally in European cuisines | Statement: [Rumex, culinaryRegion, used traditionally in European cuisines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: culinaryRegion Context triple: [Rumex, culinaryRegion, used traditionally in European cuisines]
-
A.
regionOfCulinaryImportance
chosen
Indicates that a location is recognized for its significant culinary relevance, such as notable food traditions, specialties, or gastronomic culture.
-
B.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
-
C.
cuisine
Indicates the type or style of food traditionally associated with or served by an entity (such as a restaurant or region).
-
D.
culturalRegion
Indicates that an entity is located in, associated with, or belongs to a specific cultural region or cultural area.
-
E.
foodCulture
Indicates the relationship between a place or group and its characteristic traditions, practices, and preferences surrounding food and eating.
- 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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d886e615b08190a072924329a94a6a |
completed | April 10, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69d8087e57b48190a4c253dc0210f9d4 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:37 p.m.