Triple
T24264152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | End Times Fun |
E604791
|
entity |
| Predicate | starRatingSource |
P155377
|
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: [End Times Fun, starRatingSource, Rotten Tomatoes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: starRatingSource Context triple: [End Times Fun, starRatingSource, Rotten Tomatoes]
-
A.
starCount
Indicates the number of stars associated with an entity, typically representing a rating, quality level, or count of starred items.
-
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.
estimatedStarCount
Indicates the approximate number of stars that are believed or calculated to exist in or be associated with a given astronomical object or region.
-
E.
ratingCategory
Indicates the qualitative classification or level assigned to a rating (e.g., low, medium, high) within an evaluation or scoring system.
- 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_69e29544c29c8190b023606eafe5d36a |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28c6a4cc88190914f1c52567d613a |
completed | April 29, 2026, 10:55 p.m. |
| PD | Predicate disambiguation | batch_69f1c450aa508190bc9d372a5f6ee47a |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f1c6d4e99081909f61899eccafb73e |
completed | April 29, 2026, 8:52 a.m. |
Created at: April 18, 2026, 12:06 a.m.