Triple
T11871176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dan zai noodles |
E282409
|
entity |
| Predicate | usesNoodleType |
P91365
|
FINISHED |
| Object | thin wheat noodles |
—
|
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: thin wheat noodles | Statement: [dan zai noodles, usesNoodleType, thin wheat noodles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesNoodleType Context triple: [dan zai noodles, usesNoodleType, thin wheat noodles]
-
A.
noodleType
chosen
Indicates the specific kind or category of noodle associated with an entity.
-
B.
usesNutType
Indicates that one entity employs or incorporates a specific type of nut in its structure, function, or composition.
-
C.
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).
-
D.
typicalNoodleDonenessOptions
Indicates the usual or standard levels of doneness that noodles are commonly cooked to.
-
E.
riceType
Indicates the specific variety or classification of rice associated with an 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8d39d2934819093b9f7006f45e5cb |
completed | April 10, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69d8bb272f88819090c37c944c5a60ab |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:43 p.m.