Triple
T22230022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dianna De La Garza |
E549441
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Dianna De La Garza |
—
|
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: Dianna De La Garza | Statement: [Dianna De La Garza, name, Dianna De La Garza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dianna De La Garza Context triple: [Dianna De La Garza, name, Dianna De La Garza]
-
A.
Dianna De La Garza
chosen
Dianna De La Garza is an American author and former Dallas Cowboys cheerleader best known as the mother of singer and actress Demi Lovato.
-
B.
Alana de la Garza
Alana de la Garza is an American actress best known for her roles as prosecutors and law enforcement officials in television crime dramas such as the Law & Order franchise.
-
C.
Guadalupe Rodríguez
Guadalupe Rodríguez is best known as the mother of American singer, actress, and entertainer Jennifer Lopez.
-
D.
Raquel Garza
Raquel Garza is a Mexican actress and comedian known for her work in television, including roles in popular telenovelas and comedy programs.
-
E.
Luz María Zetina
Luz María Zetina is a Mexican actress, television host, and former beauty queen known for her work in telenovelas and entertainment programs.
- 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_69e11e4102b881909cf47d3768e25c19 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12bf173308190a3d21bfc59b39728 |
completed | April 28, 2026, 9:51 p.m. |
Created at: April 16, 2026, 8:37 p.m.