Triple
T15710992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Be Italian |
E380834
|
entity |
| Predicate | targetAudienceRatingContext |
P119880
|
FINISHED |
| Object | adult themes |
—
|
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: adult themes | Statement: [Be Italian, targetAudienceRatingContext, adult themes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetAudienceRatingContext Context triple: [Be Italian, targetAudienceRatingContext, adult themes]
-
A.
targetAudienceRank
Indicates the relative priority or importance level assigned to a particular audience segment compared to other potential audiences.
-
B.
ageRatingContext
Indicates the contextual basis or circumstances (such as region, system, or criteria) under which an age rating is assigned or interpreted.
-
C.
typicalAudience
Indicates the group of people for whom something (such as a work, product, or resource) is primarily intended or most suitable.
-
D.
usesRating
Indicates that one entity applies, relies on, or incorporates a rating assigned to another entity.
-
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_69d86d9bf930819082b30cf6d169297c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f8f5d6081908243fa59b46b7c76 |
completed | April 16, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69e00526759c819088b80d85138b8974 |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0094af5b481908ad51d5d7ba0c726 |
completed | April 15, 2026, 9:55 p.m. |
Created at: April 10, 2026, 4:45 a.m.