Triple
T34012251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sadya |
E872145
|
entity |
| Predicate | servingUtensil |
P72115
|
FINISHED |
| Object | banana leaf instead of plates |
—
|
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: banana leaf instead of plates | Statement: [Sadya, servingUtensil, banana leaf instead of plates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servingUtensil Context triple: [Sadya, servingUtensil, banana leaf instead of plates]
-
A.
eatenWithUtensil
Indicates that something was eaten using a specific utensil as the means of consumption.
-
B.
cookingAppliance
Indicates that one entity is an appliance used by another entity for cooking food.
-
C.
dishType
Indicates the classification of a dish according to its culinary category or role (e.g., appetizer, main course, dessert).
-
D.
isToolOf
Indicates that one entity functions as an instrument or means used by another entity to perform tasks or achieve goals.
-
E.
servingVessel
chosen
Indicates that one entity functions as the container or vessel used to serve another entity (such as food or drink).
- 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_69f349a08848819084b348d64c1879c3 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd19f791f48190bbb6f6047f9ddc59 |
completed | May 7, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69fd0df365948190bc9bfc7ffd46acd8 |
completed | May 7, 2026, 10:10 p.m. |
Created at: May 1, 2026, 1:51 a.m.