Triple
T8424410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirty Dancing |
E198940
|
entity |
| Predicate | leadActor |
P1507
|
FINISHED |
| Object | Jennifer Grey |
E529090
|
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: Jennifer Grey | Statement: [Dirty Dancing, leadActor, Jennifer Grey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Grey Context triple: [Dirty Dancing, leadActor, Jennifer Grey]
-
A.
Jennifer Grey
chosen
Jennifer Grey is an American actress best known for her roles in the 1980s films "Dirty Dancing" and "Ferris Bueller's Day Off."
-
B.
Jennifer Beals
Jennifer Beals is an American actress best known for her breakout role in the film "Flashdance" and her extensive work in both film and television.
-
C.
Laura Kugler
Laura Kugler was the wife of Victor Kugler, one of the helpers who hid Anne Frank and her family during World War II.
-
D.
Bijou Phillips
Bijou Phillips is an American actress, model, and singer known for her roles in independent films and her work as a fashion model in the late 1990s and 2000s.
-
E.
Téa Leoni
Téa Leoni is an American actress and producer best known for her leading roles in film and television, including the political drama series "Madam Secretary."
- 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_69ca8312d63c8190bf133b676b44a385 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb85a0d04481908a5da908cafeceaa |
completed | March 31, 2026, 8:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce035aac4c81909066c1ca1318d006 |
completed | April 2, 2026, 5:49 a.m. |
Created at: March 30, 2026, 6:07 p.m.