Triple
T18805958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maya Ishii-Peters |
E459873
|
entity |
| Predicate | portrayedByAge |
P98619
|
FINISHED |
| Object | adult actor playing a 13-year-old |
—
|
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 actor playing a 13-year-old | Statement: [Maya Ishii-Peters, portrayedByAge, adult actor playing a 13-year-old]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedByAge Context triple: [Maya Ishii-Peters, portrayedByAge, adult actor playing a 13-year-old]
-
A.
portrayedByCharacterAgeApprox
chosen
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
B.
portraysFromAge
Indicates that one entity depicts another entity starting from a specified age of the depicted entity.
-
C.
portrayedBy
Indicates that one entity serves as the actor or performer who represents or plays the role of another entity in a work or medium.
-
D.
portrayedAsAdultBy
Indicates that one entity is depicted or represented as an adult by another entity (such as an artist, author, or creator).
-
E.
portraysAgeGroup
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a3d7f8d08190a3e02fab6dc40bb5 |
completed | April 20, 2026, 3:56 a.m. |
| PD | Predicate disambiguation | batch_69e48d16dd34819096e096d0c0e4c15c |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:53 a.m.