Triple
T30548070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Villainess |
E777474
|
entity |
| Predicate | hasAgeRatingInSouthKorea |
P135770
|
FINISHED |
| Object | 18+ |
—
|
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: 18+ | Statement: [The Villainess, hasAgeRatingInSouthKorea, 18+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgeRatingInSouthKorea Context triple: [The Villainess, hasAgeRatingInSouthKorea, 18+]
-
A.
filmRatingKorea
chosen
Indicates that a film has a specific official content rating assigned by the Korean rating authority.
-
B.
ratedFor
Indicates that an entity has been evaluated and assigned a suitability or quality level for a particular purpose, context, or audience.
-
C.
hasFilmRatingAustralia
Indicates that an entity (typically a film or audiovisual work) has a specific official classification or rating assigned by the Australian film rating system.
-
D.
ageRatingContext
Indicates the contextual basis or circumstances (such as region, system, or criteria) under which an age rating is assigned or interpreted.
-
E.
USRating
Indicates that an entity has been assigned a rating, classification, or evaluation according to a United States–based standard or system.
- 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_69f2249e19108190a458ab446096bf22 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68892272c8190bf6971ede46fabe4 |
completed | May 2, 2026, 11:28 p.m. |
| PD | Predicate disambiguation | batch_69f67e42d6688190b60e91d2c388c555 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 29, 2026, 8:19 p.m.