Triple
T12188941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monthélie AOC |
E290408
|
entity |
| Predicate | servingSuggestionWhite |
P77617
|
FINISHED |
| Object | pairing with fish dishes |
—
|
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: pairing with fish dishes | Statement: [Monthélie AOC, servingSuggestionWhite, pairing with fish dishes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servingSuggestionWhite Context triple: [Monthélie AOC, servingSuggestionWhite, pairing with fish dishes]
-
A.
servingSuggestionRed
Indicates that something is recommended or suggested to be served together with a red wine.
-
B.
servesWith
chosen
Indicates that one entity is customarily presented, used, or consumed together with another as a complementary accompaniment.
-
C.
servesProduct
Indicates that one entity provides or offers a particular product to others, typically in a commercial or service context.
-
D.
servesType
Indicates that one entity provides, offers, or is used to deliver a particular type, category, or kind of thing or service.
-
E.
isTypicallyServedFor
Indicates that one item is most commonly or customarily served as a meal or course for the other (e.g., a dish typically served for breakfast, lunch, or dinner).
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d938cd2edc8190b1971349dbc0dee0 |
completed | April 10, 2026, 5:52 p.m. |
| PD | Predicate disambiguation | batch_69d91c38321c819080d500d0d64a04f6 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:50 p.m.