Triple
T7308629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assamese cuisine |
E168037
|
entity |
| Predicate | mealStructure |
P26228
|
FINISHED |
| Object | multiple small side dishes with rice |
—
|
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: multiple small side dishes with rice | Statement: [Assamese cuisine, mealStructure, multiple small side dishes with rice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mealStructure Context triple: [Assamese cuisine, mealStructure, multiple small side dishes with rice]
-
A.
typicalMealStructure
chosen
Indicates the usual sequence and composition of courses or components that make up a standard meal.
-
B.
feedingStructure
Indicates a relationship where one entity serves as the anatomical or mechanical structure used by another entity to obtain or ingest food.
-
C.
foodCustom
Indicates a culturally specific practice, rule, or tradition related to the preparation, serving, or consumption of food.
-
D.
servesDish
Indicates that one entity prepares and presents a specific dish as food for another entity.
-
E.
foodItem
Indicates that one entity is a food item that can be eaten or used as food in relation to another entity.
- 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_69c6888d8e3c81909db79714903baf31 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ebda7b748190a230a22ecea79342 |
completed | March 27, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69c6e7705f4881909793071dee50c557 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 3:01 p.m.