Triple
T4687906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Act |
E103964
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Michelle Dean |
E468042
|
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: Michelle Dean | Statement: [The Act, executiveProducer, Michelle Dean]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelle Dean Context triple: [The Act, executiveProducer, Michelle Dean]
-
A.
Michelle Dean
chosen
Michelle Dean is a Canadian journalist, critic, and screenwriter best known for co-creating and writing the true-crime drama miniseries "The Act."
-
B.
Kayte Walsh
Kayte Walsh is a British former flight attendant who is best known as the wife of American actor Kelsey Grammer.
-
C.
Tricia O'Kelley
Tricia O'Kelley is an American actress best known for her comedic television roles, particularly in popular sitcoms.
-
D.
Mary Beth Hughes
Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
-
E.
Michelle Burke
Michelle Burke is an American actress best known for her roles in 1990s films such as "Dazed and Confused" and "Coneheads."
- 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_69bd43debbf08190b4bc372e286ec234 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6397f6888190a9024a51d4d34f2b |
completed | March 20, 2026, 3:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be439d76e08190a813957bdb1b44f4 |
completed | March 21, 2026, 7:07 a.m. |
Created at: March 20, 2026, 1:16 p.m.