Triple
T2461712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fast & Furious |
E54547
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Letty Ortiz |
E241104
|
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: Letty Ortiz | Statement: [Fast & Furious, featuresCharacter, Letty Ortiz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Letty Ortiz Context triple: [Fast & Furious, featuresCharacter, Letty Ortiz]
-
A.
Letty Ortiz
chosen
Letty Ortiz is a skilled street racer, mechanic, and key member of Dominic Toretto’s crew in the Fast & Furious film franchise.
-
B.
Rosa Diaz
Rosa Diaz is a tough, enigmatic, and fiercely loyal NYPD detective known for her deadpan humor and intimidating presence on the sitcom "Brooklyn Nine-Nine."
-
C.
Dolores Solitano
Dolores Solitano is a supporting character in the film "Silver Linings Playbook," known as the caring but anxious mother of protagonist Pat Solitano.
-
D.
Juni Cortez
Juni Cortez is a young, tech-savvy secret agent and one of the sibling protagonists in the Spy Kids film series.
-
E.
Ria Torres
Ria Torres is a naturally gifted deception expert and protégé of Dr. Cal Lightman in the television series "Lie to Me," known for her intuitive ability to read microexpressions and detect lies.
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd11c47408190b10c7f6a151f2db2 |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef0d2b2748190b12611863d8bf4ad |
completed | March 9, 2026, 4:09 p.m. |
Created at: March 6, 2026, 9:44 p.m.