Triple
T34427153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julianna Margulies as Morgaine |
E883713
|
entity |
| Predicate | screenCharacterAgeRange |
P2736
|
FINISHED |
| Object | from youth to adulthood |
—
|
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: from youth to adulthood | Statement: [Julianna Margulies as Morgaine, screenCharacterAgeRange, from youth to adulthood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenCharacterAgeRange Context triple: [Julianna Margulies as Morgaine, screenCharacterAgeRange, from youth to adulthood]
-
A.
hasProtagonistAgeRange
Indicates that a work’s main character falls within a specified age range.
-
B.
hasApproximateAgeRange
Indicates that one entity is associated with another entity representing an estimated or non-exact span of ages.
-
C.
ageRange
chosen
Indicates the span of ages within which an entity or relationship is considered valid or applicable.
-
D.
typicalAgeRangeOfPlayers
Indicates the usual age range of people who typically play or participate in something.
-
E.
hasAgeGuidelines
Indicates that there are specified age-related rules or recommendations governing how something should be accessed, used, or engaged with.
- 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_69f349c3dd2c819092cc9e64809f4a42 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7817daf00819098936402e75ab0a6 |
completed | May 3, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 1, 2026, 2 a.m.