Triple
T22395470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friday the 13th (2009 film) |
E553617
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Amanda Righetti |
—
|
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: Amanda Righetti | Statement: [Friday the 13th (2009 film), stars, Amanda Righetti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Righetti Context triple: [Friday the 13th (2009 film), stars, Amanda Righetti]
-
A.
Amanda Righetti
chosen
Amanda Righetti is an American actress best known for her role as agent Grace Van Pelt on the television crime drama series "The Mentalist."
-
B.
Amanda Robinson
Amanda Robinson is the spouse of Jason Robinson.
-
C.
Tonya Antonucci
Tonya Antonucci is a sports executive best known for serving as the founding commissioner of Women's Professional Soccer in the United States.
-
D.
Amanda Kelly
Amanda Kelly is a technology entrepreneur best known as a co-founder of Streamlit, an open-source framework for building data and machine learning web apps in Python.
-
E.
Amanda Roth
Amanda Roth is known as the sibling of prominent American theater producer Jordan Roth.
- 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_69e11e4cf87c8190a1ff474daec326b7 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1585e84b081908c95ed3e0d987ed8 |
completed | April 29, 2026, 1:01 a.m. |
Created at: April 16, 2026, 8:45 p.m.