Triple
T14657224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dopesick |
E344141
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Mare Winningham |
E293067
|
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: Mare Winningham | Statement: [Dopesick, starring, Mare Winningham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mare Winningham Context triple: [Dopesick, starring, Mare Winningham]
-
A.
Mare Winningham
chosen
Mare Winningham is an American actress and singer-songwriter known for her acclaimed work in film, television, and theater, including multiple Emmy-winning performances.
-
B.
Talisha Searcy
Talisha Searcy is an American local politician who serves as the mayor of Takoma Park, Maryland.
-
C.
Lilly McDowell
Lilly McDowell is an American actress known for roles in film and television and as the daughter of actors Mary Steenburgen and Malcolm McDowell.
-
D.
Jemima Kirke
Jemima Kirke is a British-American artist and actress best known for playing Jessa Johansson on the HBO series "Girls."
-
E.
Alafair Burke
Alafair Burke is an American crime novelist, law professor, and former prosecutor known for her contemporary suspense novels and collaborations on bestselling mystery series.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51a562c819098971447db4b29f7 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feadfd53a48190b7efaa4c470da784 |
completed | May 9, 2026, 3:46 a.m. |
Created at: April 10, 2026, 1:27 a.m.