Triple
T22961985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Complications |
E570925
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Lauren Sanchez |
—
|
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 Sanchez | Statement: [Complications, starring, Lauren Sanchez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Sanchez Context triple: [Complications, starring, Lauren Sanchez]
-
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.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
-
C.
Caitlin Sanchez
Caitlin Sanchez is an American actress best known for voicing the title character in the children’s animated television series "Dora the Explorer."
-
D.
Rachel Salas
Rachel Salas is a central character in the science-fiction film "In Time," portrayed as the wealthy and protective mother of Sylvia Weis.
-
E.
Rachel Salas
Rachel Salas is a fictional character from the science fiction film "In Time," known as the mother of the protagonist Will Salas.
- 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_69e245b212a88190b5259caf51606084 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181f594fc8190816418486b798198 |
completed | April 29, 2026, 3:58 a.m. |
Created at: April 17, 2026, 3:47 p.m.