Triple
T28902811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Sara as Princess Lili |
E732993
|
entity |
| Predicate | screenDebutForActor |
P103830
|
FINISHED |
| Object | Mia Sara |
—
|
NE NERFINISHED |
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: Mia Sara | Statement: [Mia Sara as Princess Lili, screenDebutForActor, Mia Sara]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenDebutForActor Context triple: [Mia Sara as Princess Lili, screenDebutForActor, Mia Sara]
-
A.
screenDebutInMajorRoleFor
Indicates that one entity made their first significant on-screen appearance (major role) in a particular production or work.
-
B.
filmDebutIn
chosen
Indicates the first film in which a person appeared or participated, marking their debut in cinema.
-
C.
featureFilmDebut
Indicates that a work marks an entity’s first appearance or role in a feature-length film.
-
D.
leadActorDebutFilmFor
Indicates that a person’s first film as a lead actor is the specified movie.
-
E.
filmDebutActorForCharacter
Indicates that an actor is making their first film appearance in the role of a specific character.
- 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_69f05b08c2008190ac426a035a2ed66d |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f676f968d08190a4adba0439b438c9 |
completed | May 2, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 28, 2026, 8:04 a.m.