Triple
T9751859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andy Davis |
E236459
|
entity |
| Predicate | ageInToyStory3 |
P59141
|
FINISHED |
| Object | about 17 years 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: about 17 years old | Statement: [Andy Davis, ageInToyStory3, about 17 years old]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageInToyStory3 Context triple: [Andy Davis, ageInToyStory3, about 17 years old]
-
A.
ageThreeTerm
Indicates that the subject has an age that falls within a defined "three-term" age category or range (e.g., early/mid/late in a given life stage).
-
B.
ageInFirstFilm
Indicates the age a person was when they appeared in their first film.
-
C.
protagonistAgeRelativeToPrequel
Indicates how the protagonist’s age in the current work compares to their age in a preceding prequel story.
-
D.
youngerVersionPortrayedBy
Indicates that one person portrays a younger version of another person, typically in a film, television show, or similar narrative work.
-
E.
ageInSeries
chosen
Indicates the age of an entity as it appears or is depicted within a specific series or installment of a work.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9facd5b881909f0569b23f308815 |
completed | April 1, 2026, 10:43 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:24 p.m.