Triple
T15961667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicki Grant |
E387075
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Chloë Sevigny |
E350873
|
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: Chloë Sevigny | Statement: [Nicki Grant, portrayedBy, Chloë Sevigny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chloë Sevigny Context triple: [Nicki Grant, portrayedBy, Chloë Sevigny]
-
A.
Chloë Sevigny
chosen
Chloë Sevigny is an American actress and fashion icon known for her work in independent films and her distinctive, avant-garde style.
-
B.
Maria Bello
Maria Bello is an American actress known for her versatile roles in film and television, including performances in projects like "A History of Violence," "ER," and "NCIS."
-
C.
Elizabeth Perkins
Elizabeth Perkins is an American actress known for her versatile film and television roles, including work in both live-action and animated projects.
-
D.
Alison Lohman
Alison Lohman is an American actress known for her roles in films such as Big Fish, White Oleander, and Drag Me to Hell.
-
E.
Samantha Morton
Samantha Morton is an acclaimed English actress and director known for her intense, emotionally rich performances in independent films and major productions alike.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15700651c819091c1cc4f60894c35 |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3c3aae081909366c01b3fec4d47 |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:53 a.m.