Triple
T14158503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chloë Sevigny |
E350873
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Chloë Stevens 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 Stevens Sevigny | Statement: [Chloë Sevigny, birthName, Chloë Stevens Sevigny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chloë Stevens Sevigny Context triple: [Chloë Sevigny, birthName, Chloë Stevens 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.
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.
-
C.
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."
-
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.
Catherine Keener
Catherine Keener is an American actress known for her nuanced supporting and character roles in acclaimed films such as "Being John Malkovich," "Capote," and "Get Out."
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61377de48190a3470d28f0edd34a |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd2804956c81909409fba998a87866 |
completed | May 8, 2026, 12:02 a.m. |
Created at: April 10, 2026, 12:58 a.m.