Triple
T8235687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garza |
E192398
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Jessica Garza |
E213983
|
NE 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: Jessica Garza | Statement: [Garza, hasNotableBearer, Jessica Garza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Garza Context triple: [Garza, hasNotableBearer, Jessica Garza]
-
A.
Jessica Garza
chosen
Jessica Garza is an American actress best known for her prominent role in the television adaptation of the horror franchise "The Purge."
-
B.
Angelica Fuentes
Angelica Fuentes is a Mexican businesswoman and philanthropist known for her leadership roles in the energy sector and in professional soccer, as well as her advocacy for women's empowerment in Latin America.
-
C.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
D.
Celina Carvajal
Celina Carvajal, also known professionally as Lena Hall, is a Tony Award–winning American actress and singer best known for her work in Broadway musicals and rock-inspired performances.
-
E.
Melissa Navia
Melissa Navia is an American actress best known for her role as Lt. Erica Ortegas on the television series "Star Trek: Strange New Worlds."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82dc8f148190a2c75a98501a7b91 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb782a5e18819096235679f5a644a8 |
completed | March 31, 2026, 7:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6cb7c80c81909d3d56c05a23f5c7 |
completed | April 2, 2026, 1:18 p.m. |
Created at: March 30, 2026, 5:46 p.m.