Triple
T5693121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Moms |
E125472
|
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: [Bad Moms, starring, Mila Kunis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mila Kunis Context triple: [Bad Moms, 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.
Kaley Cuoco
Kaley Cuoco is an American actress best known for her comedic television roles, particularly as Penny on the hit sitcom "The Big Bang Theory."
-
C.
Tausha Kutcher
Tausha Kutcher is the older sister of American actor and entrepreneur Ashton Kutcher.
-
D.
Zooey Deschanel
Zooey Deschanel is an American actress, singer, and songwriter known for her quirky, offbeat roles in films like "500 Days of Summer" and the TV series "New Girl."
-
E.
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.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c023e678c48190824d35d276985311 |
completed | March 22, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1133b6ce0819080ec5d6bad6d2e97 |
completed | March 23, 2026, 10:17 a.m. |
Created at: March 22, 2026, 3:44 p.m.