Triple
T20390016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Dead Don’t Die |
E498059
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Sara Driver |
—
|
NE NERFINISHED |
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: Sara Driver | Statement: [The Dead Don’t Die, starring, Sara Driver]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sara Driver Context triple: [The Dead Don’t Die, starring, Sara Driver]
-
A.
Sara Driver
chosen
Sara Driver is an American filmmaker and producer known for her work in New York’s independent cinema scene and her collaborations with director Jim Jarmusch.
-
B.
Sally Beauman
Sally Beauman was a British journalist and bestselling novelist known for works such as "Rebecca’s Tale" and "Destiny."
-
C.
Aviva Baumann
Aviva Baumann is an American actress best known for her role as Nicola in the comedy film "Superbad."
-
D.
Elizabeth Dowdeswell
Elizabeth Dowdeswell is a Canadian public servant and former Under-Secretary-General of the United Nations who has served as the 29th Lieutenant Governor of Ontario.
-
E.
Lissa Evans
Lissa Evans is a British author and former television director and producer, known for her witty, character-driven novels for both adults and children.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790e65a081909832855758fffd14 |
completed | April 20, 2026, 7:05 p.m. |
Created at: April 16, 2026, 11:28 a.m.