Triple
T516406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Million Ways to Die in the West |
E10718
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Amanda Seyfried |
E32689
|
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: Amanda Seyfried | Statement: [A Million Ways to Die in the West, starring, Amanda Seyfried]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Seyfried Context triple: [A Million Ways to Die in the West, starring, Amanda Seyfried]
-
A.
Amanda Seyfried
chosen
Amanda Seyfried is an American actress and singer known for her roles in films such as "Mamma Mia!", "Les Misérables," and "Mean Girls."
-
B.
Emily Blunt
Emily Blunt is a British actress known for her versatile performances in films such as "The Devil Wears Prada," "Edge of Tomorrow," "A Quiet Place," and "Mary Poppins Returns."
-
C.
Emma Stone
Emma Stone is an American actress acclaimed for her versatile performances in films such as "La La Land," for which she won the Academy Award for Best Actress.
-
D.
Rooney Mara
Rooney Mara is an American actress known for her acclaimed performances in films such as "The Girl with the Dragon Tattoo" and "Carol."
-
E.
Olivia Thirlby
Olivia Thirlby is an American actress known for her roles in films such as "Juno," "Dredd," and various independent and mainstream productions.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f184c3a481909bf60bb627b0ea88 |
completed | Feb. 28, 2026, 1:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4b23bf69481908db3a0f3de8c2bf1 |
completed | March 1, 2026, 9:40 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.