Triple
T9759575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chocolat |
E236634
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Carrie-Anne Moss |
E195727
|
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: Carrie-Anne Moss | Statement: [Chocolat, portrayedBy, Carrie-Anne Moss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carrie-Anne Moss Context triple: [Chocolat, portrayedBy, Carrie-Anne Moss]
-
A.
Carrie-Anne Moss
chosen
Carrie-Anne Moss is a Canadian actress best known for her iconic role as Trinity in the science fiction film franchise "The Matrix."
-
B.
Nikki Reed
Nikki Reed is an American actress and screenwriter best known for her role as Rosalie Hale in the Twilight film series.
-
C.
Sean Young
Sean Young is an American actress best known for her roles in 1980s films such as "Blade Runner," "Dune," and "No Way Out."
-
D.
Sigourney Weaver
Sigourney Weaver is an acclaimed American actress best known for her iconic role as Ellen Ripley in the "Alien" film series and her influential work in science fiction and genre cinema.
-
E.
Evangeline Lilly
Evangeline Lilly is a Canadian actress best known for her roles in the television series "Lost" and as Hope van Dyne/The Wasp in the Marvel Cinematic Universe.
- 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_69ca84d64f6c8190a4ed4e9f5936eda5 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda049995c81908569ec61805642b2 |
completed | April 1, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bcdbdbcc8190b2c454729a50f7fb |
completed | April 5, 2026, 1:37 a.m. |
Created at: March 30, 2026, 8:24 p.m.