Triple
T5193425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Furious 7 |
E117210
|
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: [Furious 7, featuresCharacter, Letty Ortiz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Letty Ortiz Context triple: [Furious 7, 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.
Letty Aronson
Letty Aronson is an American film producer best known for her long-running collaboration with director Woody Allen on numerous critically acclaimed movies.
-
C.
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."
-
D.
Abuelita Toretto
Abuelita Toretto is the warm but strong-willed matriarch of the Toretto family in the Fast & Furious film franchise.
-
E.
Angel Lopez
Angel Lopez is a music producer known for his work on the project "Hands On."
- 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_69bd4462ed04819084fcb01eb9d2fa74 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79f142488190bc6c57b8ff7ef894 |
completed | March 20, 2026, 4:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beefbcbd1881908afc477150135961 |
completed | March 21, 2026, 7:21 p.m. |
Created at: March 20, 2026, 1:46 p.m.