Triple
T22968044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patrick Whitesell |
E571102
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Lauren Sánchez |
—
|
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: Lauren Sánchez | Statement: [Patrick Whitesell, spouse, Lauren Sánchez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Sánchez Context triple: [Patrick Whitesell, spouse, Lauren Sánchez]
-
A.
Lauren Sánchez
chosen
Lauren Sánchez is an American media personality, former news anchor, and helicopter pilot who gained widespread attention for her relationship with Amazon founder Jeff Bezos.
-
B.
Melissa Villaseñor
Melissa Villaseñor is an American comedian, impressionist, and actress best known as a former cast member of Saturday Night Live.
-
C.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
-
D.
Nadine Velazquez
Nadine Velazquez is an American actress and model best known for her roles in the sitcom "My Name Is Earl" and the film "Flight."
-
E.
Elena Padilla
Elena Padilla is a relatively obscure individual about whom no widely known public or biographical information is readily available.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f182301f388190bb39e3d5b356dc65 |
completed | April 29, 2026, 3:59 a.m. |
Created at: April 17, 2026, 3:48 p.m.