Triple
T8129248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 13 Going on 30 |
E189813
|
entity |
| Predicate | mainCharacterAgeAfterTransformation |
P47235
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [13 Going on 30, mainCharacterAgeAfterTransformation, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCharacterAgeAfterTransformation Context triple: [13 Going on 30, mainCharacterAgeAfterTransformation, 30]
-
A.
ageAtFirstTransformation
chosen
Indicates the age an entity was when it underwent its first transformation or change of state.
-
B.
hasAgeTransformation
Indicates a relationship where an entity undergoes or causes a change in age or age-related state.
-
C.
becomesImmortalIn
Indicates that an entity transitions into a state of immortality within a specified context, time, or medium.
-
D.
protagonistAge
Indicates the age of the main character or central figure in a narrative or scenario.
-
E.
protagonistAgeRelativeToPrequel
Indicates how the protagonist’s age in the current work compares to their age in a preceding prequel story.
- 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_69ca82bcb4848190a9a9d036ad768642 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4c4c2e388190b86854f8b1765e61 |
completed | March 31, 2026, 4:23 a.m. |
| PD | Predicate disambiguation | batch_69cb3696379c8190a20965e59ed8f370 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:34 p.m.