Triple
T31777175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuan Mei |
E811093
|
entity |
| Predicate | culinaryPhilosophy |
P172291
|
FINISHED |
| Object | respect for ingredients |
—
|
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: respect for ingredients | Statement: [Yuan Mei, culinaryPhilosophy, respect for ingredients]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: culinaryPhilosophy Context triple: [Yuan Mei, culinaryPhilosophy, respect for ingredients]
-
A.
cuisine
Indicates the type or style of food traditionally associated with or served by an entity (such as a restaurant or region).
-
B.
culinaryUse
Indicates that one entity is used in the preparation, flavoring, or serving of food or drink for another entity.
-
C.
culinaryStatus
Indicates the current state or condition of something in relation to cooking or food preparation (e.g., raw, cooked, undercooked, burnt).
-
D.
traditionalCuisine
Indicates that an entity is associated with the customary or historically rooted style of cooking and food preparation characteristic of a particular culture, region, or community.
-
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. 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_69f348e544a48190ab6e700b05f6438c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6abe217948190a5e5537ca4eb97a2 |
completed | May 3, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69f6aa21f2508190a204a424ffc00ca6 |
completed | May 3, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69f6aac95c1481909fff33702d0a6c37 |
completed | May 3, 2026, 1:54 a.m. |
Created at: April 30, 2026, 11:35 p.m.