Triple
T7436875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gattaca |
E171637
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Uma Thurman |
E290199
|
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: Uma Thurman | Statement: [Gattaca, starring, Uma Thurman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uma Thurman Context triple: [Gattaca, starring, Uma Thurman]
-
A.
Uma Thurman
chosen
Uma Thurman is an American actress and model best known for her roles in films such as "Pulp Fiction" and the "Kill Bill" series.
-
B.
Gina Gershon
Gina Gershon is an American actress known for her versatile roles in film, television, and theater, including standout performances in movies like "Bound" and "Showgirls."
-
C.
Jennifer Jason Leigh
Jennifer Jason Leigh is an American actress known for her intense, character-driven performances in films such as "Fast Times at Ridgemont High," "Single White Female," and "The Hateful Eight."
-
D.
Famke Janssen
Famke Janssen is a Dutch actress and former fashion model best known for her roles in the X-Men film series and the James Bond film GoldenEye.
-
E.
Andie MacDowell
Andie MacDowell is an American actress and former fashion model best known for her roles in romantic comedies such as "Groundhog Day" and "Four Weddings and a Funeral."
- 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_69c68a64228c8190affaec2a8127ce7b |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f349399c8190b46d5882ece2e73a |
completed | March 27, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81f23afb48190ae57fb952b2721c3 |
completed | March 28, 2026, 6:34 p.m. |
Created at: March 27, 2026, 3:13 p.m.