Triple
T2872044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afro-Saint Kitts and Nevis people |
E63584
|
entity |
| Predicate | influencedCuisine |
P39848
|
FINISHED |
| Object | Saint Kitts and Nevis cuisine |
—
|
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: Saint Kitts and Nevis cuisine | Statement: [Afro-Saint Kitts and Nevis people, influencedCuisine, Saint Kitts and Nevis cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedCuisine Context triple: [Afro-Saint Kitts and Nevis people, influencedCuisine, Saint Kitts and Nevis cuisine]
-
A.
cuisineInfluence
chosen
Indicates that one cuisine has had a notable impact on the development, style, or characteristics of another cuisine.
-
B.
placeOfInfluence
Indicates the location or area where an entity exerts significant impact, authority, or cultural, social, or intellectual influence.
-
C.
influencedTradition
Indicates that one entity has had a shaping or guiding effect on the development, practices, or values of a particular tradition.
-
D.
influencedByGenre
Indicates that something’s characteristics, style, or development are shaped or affected by a particular genre.
-
E.
cuisine
Indicates the type or style of food traditionally associated with or served by an entity (such as a restaurant or region).
- 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_69ab4c42fb8c8190b36e161d47c03b81 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdfe46a1c819084399a191f0dfe9c |
completed | March 7, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69abdd142e4c8190b424cb0c5ff40d04 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.