Triple
T8431001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen Sugar |
E199112
|
entity |
| Predicate | leadActor |
P1507
|
FINISHED |
| Object | Rutina Wesley |
E447636
|
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: Rutina Wesley | Statement: [Queen Sugar, leadActor, Rutina Wesley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rutina Wesley Context triple: [Queen Sugar, leadActor, Rutina Wesley]
-
A.
Rutina Wesley
chosen
Rutina Wesley is an American actress best known for her role as Tara Thornton on the HBO series "True Blood."
-
B.
Movin’ Wes
Movin’ Wes is a 1964 jazz album by guitarist Wes Montgomery that showcases his transition toward a more orchestrated, accessible sound.
-
C.
Marcus Wesley
Marcus Wesley is an American actor best known for his supporting roles in films such as the 2006 fantasy adventure movie "The Fall."
-
D.
Wesley Jonathan
Wesley Jonathan is an American actor best known for his roles in early-2000s television sitcoms and films, including the roller-skating comedy-drama "Roll Bounce."
-
E.
Wesley
Wesley is a masculine given name of English origin, traditionally used in English-speaking countries.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbd1a4876c81908d5a708bb1f35683 |
completed | March 31, 2026, 1:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0380ed948190bdba247d67769ade |
completed | April 2, 2026, 5:49 a.m. |
Created at: March 30, 2026, 6:07 p.m.