Triple
T8270340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trisha Yearwood |
E193410
|
entity |
| Predicate | network |
P2637
|
FINISHED |
| Object | Food Network |
E52602
|
NE 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: Food Network | Statement: [Trisha Yearwood, network, Food Network]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Food Network Context triple: [Trisha Yearwood, network, Food Network]
-
A.
Food Network
chosen
Food Network is a cable television channel dedicated to food, cooking, and culinary lifestyle programming.
-
B.
Cooking Channel
Cooking Channel is an American cable television network focused on instructional cooking shows, culinary exploration, and food-related entertainment.
-
C.
Food Network Magazine
Food Network Magazine is a lifestyle and cooking publication that features recipes, chef profiles, and food-related entertainment inspired by the Food Network television channel.
-
D.
Recipe.TV
Recipe.TV is a lifestyle television network and streaming channel focused on cooking, culinary travel, and food-related programming.
-
E.
Bravo (TV network)
Bravo is an American cable television network best known for its reality programming and pop-culture-focused series such as the Real Housewives franchise and Top Chef.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82e14ae481908ffdb822cd2192bc |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb795243fc8190a66afef7476e1147 |
completed | March 31, 2026, 7:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd683d04e081908b0ce81e866f0311 |
completed | April 1, 2026, 6:47 p.m. |
Created at: March 30, 2026, 5:50 p.m.