Triple
T20696647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bacon Maple Bar |
E508677
|
entity |
| Predicate | hasCulinaryTrend |
P141124
|
FINISHED |
| Object | bacon desserts |
—
|
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: bacon desserts | Statement: [Bacon Maple Bar, hasCulinaryTrend, bacon desserts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCulinaryTrend Context triple: [Bacon Maple Bar, hasCulinaryTrend, bacon desserts]
-
A.
haveCuisine
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of cuisine.
-
B.
hasCulinaryOrigin
Indicates that something originates from, or is traditionally associated with, a particular culinary tradition, cuisine, or food culture.
-
C.
hasSpecialtyFood
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
D.
hasCuisineItem
Indicates that a particular cuisine includes, features, or is associated with a specific food item.
-
E.
hasCuisineRecognition
Indicates that an entity has received formal recognition, awards, or notable acknowledgment specifically for its cuisine.
- F. None of above. chosen
Provenance (4 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_69e0b4c2b2a481909e31e9cb8f81ab55 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c1123d7c81908a1d16923437266d |
completed | April 21, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69e5c044d1108190b2b5d25de23f6401 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:10 p.m.