Triple
T28906350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MILF of the Year |
E733091
|
entity |
| Predicate | hasContentRatingContext |
P36218
|
FINISHED |
| Object | explicit adult content |
—
|
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: explicit adult content | Statement: [MILF of the Year, hasContentRatingContext, explicit adult content]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasContentRatingContext Context triple: [MILF of the Year, hasContentRatingContext, explicit adult content]
-
A.
hasContentRating
chosen
Indicates that something is associated with a specified content rating that reflects its suitability for particular audiences.
-
B.
contentRatingBody
Indicates which organization or authority assigned the content rating for the item.
-
C.
containsAdultContent
Indicates that the referenced item includes material intended for adults, such as explicit sexual, violent, or otherwise age-restricted content.
-
D.
contentRatingImpact
Indicates how the content rating of a work influences its reception, accessibility, or effects on audiences or platforms.
-
E.
ageRatingContext
Indicates the contextual basis or circumstances (such as region, system, or criteria) under which an age rating is assigned or interpreted.
- 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_69f05b096d208190958a57d2e4b5a93a |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_6a000efe971081909de03f875a7ad6cc |
completed | May 10, 2026, 4:52 a.m. |
| PD | Predicate disambiguation | batch_6a000c4ffe788190a5757af60aadd9f3 |
completed | May 10, 2026, 4:40 a.m. |
Created at: April 28, 2026, 8:07 a.m.