Triple
T15384887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ESRB K-A |
E367891
|
entity |
| Predicate | visualMark |
P99152
|
FINISHED |
| Object | black-and-white rating icon with text "Kids to Adults" |
—
|
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: black-and-white rating icon with text "Kids to Adults" | Statement: [ESRB K-A, visualMark, black-and-white rating icon with text "Kids to Adults"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualMark Context triple: [ESRB K-A, visualMark, black-and-white rating icon with text "Kids to Adults"]
-
A.
visualTrademark
Indicates that one entity serves as a visual trademark or logo representing another entity.
-
B.
visualElements
Indicates that one entity contains, uses, or is characterized by specific visual components or graphical features associated with another entity.
-
C.
markingFeature
chosen
Indicates a feature that serves as a distinguishing mark or identifier associated with an entity.
-
D.
distinctiveMarking
Indicates that one entity bears a unique or distinguishing visual feature or pattern that sets it apart from others.
-
E.
visualMedium
Indicates that one entity serves as the visual medium or format through which another entity is presented, communicated, or experienced.
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e74ff70819094c1a85f51d6e228 |
completed | April 16, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69ded27742a881909cd73cc5c7d062fd |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:19 a.m.