Triple
T15998407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marsai Martin |
E388034
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marsai |
E388034
|
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: Marsai | Statement: [Marsai Martin, givenName, Marsai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marsai Context triple: [Marsai Martin, givenName, Marsai]
-
A.
Marsai Martin
chosen
Marsai Martin is an American actress and producer best known for her role as Diane Johnson on the television series "Black-ish" and for becoming one of the youngest executive producers in Hollywood with the film "Little."
-
B.
Keisha
Keisha is a feminine given name used in English-speaking communities, often associated with African-American culture.
-
C.
Sharissa
Sharissa is a musical artist known for her featured performance on the track "Masquerade."
-
D.
Mia Sara
Mia Sara is an American actress best known for her role as Sloane Peterson in the 1986 teen comedy film "Ferris Bueller's Day Off."
-
E.
Saraya
Saraya is a professional wrestler best known for her groundbreaking WWE career under the name Paige and later work in All Elite Wrestling.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157893ebc8190acb75ee05e450fae |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbca65a4819090109589dba7f7a9 |
completed | May 10, 2026, 1:13 a.m. |
Created at: April 10, 2026, 4:55 a.m.