Triple
T22023094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little (2019 film) |
E543889
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Germaine Franco |
—
|
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: Germaine Franco | Statement: [Little (2019 film), musicBy, Germaine Franco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Germaine Franco Context triple: [Little (2019 film), musicBy, Germaine Franco]
-
A.
Germaine Franco
chosen
Germaine Franco is an American film composer and songwriter known for her groundbreaking work on animated features, including composing the score for Disney’s "Encanto."
-
B.
Antoinette Valente
Antoinette Valente is an actress known for appearing in the romantic comedy film "The Truth About Cats & Dogs."
-
C.
Camille Boustany
Camille Boustany is a notable individual recognized for bearing the Boustany surname.
-
D.
Jacqueline Belhomme
Jacqueline Belhomme is a French politician who serves as the mayor of the Paris suburb of Malakoff.
-
E.
Jean Roqua
Jean Roqua is a disciplined Brazilian jiu-jitsu and mixed martial arts trainer who mentors the protagonist in the action film "Never Back Down."
- 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.