Triple
T29149746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Smile (2022 film) |
E738870
|
entity |
| Predicate | ratingPlatform |
P166557
|
FINISHED |
| Object | Rotten Tomatoes |
—
|
NE NERFINISHED |
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: Rotten Tomatoes | Statement: [Smile (2022 film), ratingPlatform, Rotten Tomatoes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ratingPlatform Context triple: [Smile (2022 film), ratingPlatform, Rotten Tomatoes]
-
A.
ratingContent
Indicates that an entity evaluates or assigns a quality or satisfaction score to some content.
-
B.
ratingContext
Indicates the situational or contextual factors under which a rating is given or applies.
-
C.
ratingDescription
Indicates the textual explanation or qualitative summary associated with a given rating or score.
-
D.
ratingSystem
Indicates a system or method used to assign evaluative scores or rankings to items, actions, or entities based on defined criteria.
-
E.
reviewScale
Indicates the rating system or range (such as 1–5 stars, 0–10, etc.) used to evaluate or score something in a review.
- 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_69f07cb46f148190874eb8576a447567 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f662a439c88190985b014077e75ecc |
completed | May 2, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69f660f082508190a95a7888ad66cb2e |
completed | May 2, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69f6617a7e7c81908cfac4a2250797ee |
completed | May 2, 2026, 8:41 p.m. |
Created at: April 28, 2026, 11:41 a.m.