Triple
T654381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted |
E11614
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Mila Kunis |
E25478
|
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: Mila Kunis | Statement: [Ted, starring, Mila Kunis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mila Kunis Context triple: [Ted, starring, Mila Kunis]
-
A.
Mila Kunis
chosen
Mila Kunis is an American actress known for her roles in films like "Black Swan" and "Forgetting Sarah Marshall" and for voicing Meg Griffin on the animated series "Family Guy."
-
B.
Elizabeth Banks
Elizabeth Banks is an American actress, director, and producer known for her roles in films such as "The Hunger Games" series, "Pitch Perfect," and numerous comedic and dramatic projects in film and television.
-
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.
Alison Brie
Alison Brie is an American actress known for her roles in television series like "Community" and "Mad Men," as well as her voice work in animated films.
-
E.
Sarah Silverman
Sarah Silverman is an American stand-up comedian, actress, and writer known for her sharp, provocative humor and appearances in film and television.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4bb5b881908a18b5ec1c94e0cf |
completed | March 1, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a591486b708190b0191e958c6c8851 |
completed | March 2, 2026, 1:31 p.m. |
Created at: March 1, 2026, 7:36 p.m.