Triple
T3884175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vertigo |
E92898
|
entity |
| Predicate | leadActress |
P6108
|
FINISHED |
| Object | Kim Novak |
E414298
|
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: Kim Novak | Statement: [Vertigo, leadActress, Kim Novak]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Novak Context triple: [Vertigo, leadActress, Kim Novak]
-
A.
Kim Novak
chosen
Kim Novak is an American actress best known for her roles in classic 1950s and 1960s films, particularly Alfred Hitchcock’s "Vertigo."
-
B.
Ava Gardner
Ava Gardner was a celebrated American film actress and Hollywood icon of the 1940s and 1950s, renowned for her beauty, charisma, and roles in classics such as "The Killers" and "Mogambo."
-
C.
Lauren Bacall
Lauren Bacall was an iconic American film and stage actress known for her sultry voice, striking looks, and classic roles in 1940s Hollywood noir films.
-
D.
Sylvia Sidney
Sylvia Sidney was an American actress known for her work in 1930s crime dramas and later roles in films like "Beetlejuice" and "Mars Attacks!".
-
E.
Dina Merrill
Dina Merrill was an American actress, heiress, and philanthropist known for her elegant screen presence in mid-20th-century Hollywood films and television.
- 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_69aed9697de0819087c2559295ff3d12 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeec9029908190a7b36a3827734db1 |
completed | March 9, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b65f1304b481909715c263a743581d |
completed | March 15, 2026, 7:26 a.m. |
Created at: March 9, 2026, 3:20 p.m.