Triple
T18822080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daisy Fay |
E460285
|
entity |
| Predicate | ageRangeInStory |
P96629
|
FINISHED |
| Object | childhood to young 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: childhood to young adulthood | Statement: [Daisy Fay, ageRangeInStory, childhood to young adulthood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageRangeInStory Context triple: [Daisy Fay, ageRangeInStory, childhood to young adulthood]
-
A.
hasProtagonistAgeRange
Indicates that a work’s main character falls within a specified age range.
-
B.
ageRange
Indicates the span of ages within which an entity or relationship is considered valid or applicable.
-
C.
childInStory
Indicates that one entity is a child character who appears within the narrative context of the other entity (a story).
-
D.
ageDuringNarration
Indicates that an entity has a specified age at the time when the described narrative or event is taking place.
-
E.
fictionalAgeRange
chosen
Indicates the span of ages a fictional character or entity is depicted as having within a narrative or fictional context.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6ba90988190a93e1c2fa23b3d1d |
completed | April 20, 2026, 4:08 a.m. |
| PD | Predicate disambiguation | batch_69e48d1b10ec8190985c6fb5766ff981 |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:55 a.m.