Triple
T23442655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H. Jon Benjamin as Bob Belcher |
E565443
|
entity |
| Predicate | characterFoodSpecialty |
P79219
|
FINISHED |
| Object | burgers |
—
|
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: burgers | Statement: [H. Jon Benjamin as Bob Belcher, characterFoodSpecialty, burgers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterFoodSpecialty Context triple: [H. Jon Benjamin as Bob Belcher, characterFoodSpecialty, burgers]
-
A.
knownForDish
chosen
Indicates that an entity is recognized or notable for preparing, serving, or being associated with a particular dish.
-
B.
hasSpecialtyFood
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
C.
chefTitle
Indicates that one entity holds a specific professional or honorific title in the role of a chef relative to another entity.
-
D.
roleInFood
Indicates the functional role or purpose that an entity has within a food item, product, or context (e.g., ingredient, flavoring, preservative).
-
E.
chef
Indicates that one entity serves as the cook or culinary professional responsible for preparing food for another entity or context.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64654e88190b530958b27b32412 |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:51 p.m.