Triple
T22482516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chongqing hot pot |
E555800
|
entity |
| Predicate | typicalIngredientCategory |
P40800
|
FINISHED |
| Object | sliced meats |
—
|
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: sliced meats | Statement: [Chongqing hot pot, typicalIngredientCategory, sliced meats]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalIngredientCategory Context triple: [Chongqing hot pot, typicalIngredientCategory, sliced meats]
-
A.
ingredientType
chosen
Indicates that one entity is classified as a specific type or category of ingredient in relation to another.
-
B.
genreOfRecipes
Indicates that one entity is a genre or category that characterizes the type or style of recipes associated with another entity.
-
C.
usesIngredient
Indicates that one entity employs or incorporates another entity as an ingredient in its composition or creation.
-
D.
typicalIngredientRatio
Indicates the usual proportional relationship between different ingredients used together in a preparation or mixture.
-
E.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
- 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_69e11e53897c819088863779f8c50bb0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15c3a3b688190b41599979d038d85 |
completed | April 29, 2026, 1:17 a.m. |
| PD | Predicate disambiguation | batch_69e898b6eee08190ba673a0ee329e671 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:49 p.m.