Triple
T815070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenny Rogers Roasters |
E17634
|
entity |
| Predicate | hasFoodType |
P17971
|
FINISHED |
| Object | rotisserie chicken |
—
|
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: rotisserie chicken | Statement: [Kenny Rogers Roasters, hasFoodType, rotisserie chicken]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFoodType Context triple: [Kenny Rogers Roasters, hasFoodType, rotisserie chicken]
-
A.
hasStapleFood
Indicates that an entity’s primary or regularly consumed basic food item is another specified entity.
-
B.
hasCuisineItem
Indicates that a particular cuisine includes, features, or is associated with a specific food item.
-
C.
hasFruitType
Indicates that an entity possesses or is associated with a specific type or category of fruit.
-
D.
hasSpecialtyFood
chosen
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
E.
includesFeedingType
Indicates that one entity encompasses or specifies a particular type or category of feeding associated with 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab5035608190bff5f3843b75e662 |
completed | March 1, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69a4aa756920819080ae82948974c876 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.