Triple
T26896148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jason Marsden as Kovu (adult) |
E677902
|
entity |
| Predicate | ageVersionOfCharacter |
P39940
|
FINISHED |
| Object | adult Kovu |
—
|
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 Kovu | Statement: [Jason Marsden as Kovu (adult), ageVersionOfCharacter, adult Kovu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageVersionOfCharacter Context triple: [Jason Marsden as Kovu (adult), ageVersionOfCharacter, adult Kovu]
-
A.
portrayedByCharacterAgeApprox
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
B.
youngerVersionPortrayedBy
chosen
Indicates that one person portrays a younger version of another person, typically in a film, television show, or similar narrative work.
-
C.
ageInSeries
Indicates the age of an entity as it appears or is depicted within a specific series or installment of a work.
-
D.
filmCharacterVersionOf
Indicates that one character is a specific film adaptation or portrayal of another character originating from a different version or medium.
-
E.
characterAgeDescriptor
Indicates how a character’s age is qualitatively described or categorized (e.g., young, middle-aged, elderly) rather than given as a specific number.
- 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_69eee9befee48190a26f214faa867be7 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61faa702c81909489a6d40e8ee023 |
completed | May 2, 2026, 4 p.m. |
| PD | Predicate disambiguation | batch_69f611af72ac819094598dd2530d7411 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 5:48 a.m.