Triple
T8857541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suzy Amis Cameron |
E210795
|
entity |
| Predicate | OMDPlanSubtitle |
P2765
|
FINISHED |
| Object | Swap One Meal a Day to Save Your Health and Save the Planet |
—
|
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: Swap One Meal a Day to Save Your Health and Save the Planet | Statement: [Suzy Amis Cameron, OMDPlanSubtitle, Swap One Meal a Day to Save Your Health and Save the Planet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: OMDPlanSubtitle Context triple: [Suzy Amis Cameron, OMDPlanSubtitle, Swap One Meal a Day to Save Your Health and Save the Planet]
-
A.
languageOfSubtitles
Indicates the language in which the subtitles for a given media item are provided.
-
B.
hasSubtitles
Indicates that one media item provides subtitle text or tracks that accompany another media item or its audio content.
-
C.
subtitleDFunction
Indicates a functional relationship where one entity serves as the subtitle or secondary textual label for another entity.
-
D.
subtitle
chosen
Indicates that one work serves as a secondary or explanatory title to another, typically appearing beneath the main title.
-
E.
languageDubbedIn
Indicates that the content’s audio has been dubbed into the specified language.
- 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_69ca838bbddc8190ab546d737e5d350f |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60e3b62c8190bf779e7e1db767f6 |
completed | April 1, 2026, 12:03 a.m. |
| PD | Predicate disambiguation | batch_69cc5c279ea481908c71756f694b66bf |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:50 p.m.