Triple
T22023130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vengeance (2022 film) |
E543890
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Isabella Amara |
—
|
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: Isabella Amara | Statement: [Vengeance (2022 film), castMember, Isabella Amara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabella Amara Context triple: [Vengeance (2022 film), castMember, Isabella Amara]
-
A.
Isabella Amara
chosen
Isabella Amara is an American actress known for roles in films such as "Spider-Man: Homecoming," "Avengers: Infinity War," and various independent and comedy projects.
-
B.
Isabella Turk
Isabella Turk is the daughter of Dr. Christopher Turk from the television series "Scrubs."
-
C.
Isabella Mir
Isabella Mir is the daughter of former UFC heavyweight champion and mixed martial artist Frank Mir.
-
D.
Isabella Tena
Isabella Tena is a Mexican child actress best known for her roles in popular telenovelas.
-
E.
Isabella Sermon
Isabella Sermon is a British actress best known for playing Maisie Lockwood in the Jurassic World film series.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e2e8ea4819084210fe06d3a1b8d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127c9959481908da6bed356199f75 |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:23 p.m.