Triple
T13119141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Split |
E311677
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object | Jessica Sula |
E525056
|
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: Jessica Sula | Statement: [Split, starredActor, Jessica Sula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Sula Context triple: [Split, starredActor, Jessica Sula]
-
A.
Jessica Sula
chosen
Jessica Sula is a Welsh actress best known for her roles in the British teen drama "Skins" and various American film and television projects.
-
B.
Briana DeJesus
Briana DeJesus is a reality television personality best known for chronicling her life as a young mother on MTV’s Teen Mom franchise.
-
C.
Rachel Solando
Rachel Solando is a mysterious missing patient at a remote psychiatric hospital whose disappearance drives the psychological thriller plot of "Shutter Island."
-
D.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
-
E.
Talisa Soto
Talisa Soto is an American actress and former model best known for her roles in films such as the James Bond movie "Licence to Kill" and the "Mortal Kombat" series.
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98196e69081909111407ee3d9f08e |
completed | April 10, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7396e6cf881908b4cc3836501ed08 |
completed | May 3, 2026, 12:02 p.m. |
Created at: April 9, 2026, 9:06 p.m.