Triple
T5588990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Color of Money |
E146828
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Helen Shaver |
E385395
|
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: Helen Shaver | Statement: [The Color of Money, stars, Helen Shaver]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helen Shaver Context triple: [The Color of Money, stars, Helen Shaver]
-
A.
Helen Shaver
chosen
Helen Shaver is a Canadian actress and director known for her work in film and television since the 1970s, including prominent roles in thrillers and dramas.
-
B.
Betty Reynolds
Betty Reynolds is the daughter of Canadian actor Ryan Reynolds and American actress Blake Lively.
-
C.
Eileen Heckart
Eileen Heckart was an American character actress known for her versatile work on stage, film, and television, including an Academy Award–winning performance in "Butterflies Are Free."
-
D.
Lela Rogers
Lela Rogers was an American journalist, screenwriter, and acting coach best known as the mother and early career mentor of Hollywood star Ginger Rogers.
-
E.
Verna Felton
Verna Felton was an American character actress and voice performer best known for her memorable roles in classic Disney animated films.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0209ff5d88190843b6d134390ab71 |
completed | March 22, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07d8c6f8881909ac2018d11f5aef8 |
completed | March 22, 2026, 11:38 p.m. |
Created at: March 22, 2026, 3:38 p.m.