Triple
T18216057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Concussion (2013 film) |
E436161
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Robin Weigert |
—
|
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: Robin Weigert | Statement: [Concussion (2013 film), starring, Robin Weigert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robin Weigert Context triple: [Concussion (2013 film), starring, Robin Weigert]
-
A.
Robin Weigert
chosen
Robin Weigert is an American actress best known for her acclaimed portrayal of Calamity Jane on the HBO series "Deadwood."
-
B.
Fran Weissler
Fran Weissler is a prominent American Broadway producer known for her award-winning revivals and long-running hits alongside her husband, Barry Weissler.
-
C.
Emmy Sonnemann
Emmy Sonnemann was a German stage actress best known as the second wife of Hermann Göring, a leading figure in Nazi Germany.
-
D.
Sonya Walger
Sonya Walger is a British actress best known for her roles in television series such as "Lost," "FlashForward," and "For All Mankind."
-
E.
Jo Eisinger
Jo Eisinger was an American screenwriter best known for his dark, psychologically complex film noir scripts, including classics like "Gilda" and "Night and the City."
- 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_69d8b9103a8081908bbb0836fef10efd |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e47765a081908d0bbca1245f89ba |
completed | April 19, 2026, 2:19 p.m. |
Created at: April 10, 2026, 10:32 a.m.