Triple
T10814193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soka |
E255181
|
entity |
| Predicate | hasTypeOfFood |
P59511
|
FINISHED |
| Object | senbei |
—
|
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: senbei | Statement: [Soka, hasTypeOfFood, senbei]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfFood Context triple: [Soka, hasTypeOfFood, senbei]
-
A.
hasDishType
Indicates that an item (such as a food or menu entry) is classified as belonging to a particular type of dish (e.g., appetizer, main course, dessert).
-
B.
hasMealType
Indicates that an entity is associated with a specific category or type of meal (such as breakfast, lunch, or dinner).
-
C.
hasFoodOption
Indicates that an entity offers, provides, or includes a particular type of food or dining option.
-
D.
hasStapleFood
Indicates that an entity’s primary or regularly consumed basic food item is another specified entity.
-
E.
haveCuisine
chosen
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of cuisine.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733ece4488190b553a66c4b5188bc |
completed | April 9, 2026, 5:06 a.m. |
| PD | Predicate disambiguation | batch_69d70d1bf3648190b36fa96ea018e0dc |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:18 p.m.