Triple
T17977484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Salas |
E449513
|
entity |
| Predicate | portrayedInFilmBy |
P9616
|
FINISHED |
| Object | Rachel Roberts |
—
|
NE NERFINISHED |
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: Rachel Roberts | Statement: [Rachel Salas, portrayedInFilmBy, Rachel Roberts]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Roberts Context triple: [Rachel Salas, portrayedInFilmBy, Rachel Roberts]
-
A.
Rachel Roberts
Rachel Roberts was a Welsh actress known for her intense, emotionally powerful performances in British and international films during the mid-20th century.
-
B.
Rachel Roberts
chosen
Rachel Roberts is a Canadian model and actress known for her work in fashion campaigns and films such as "Simone" and "Entourage."
-
C.
Laura Roberts
Laura Roberts is known primarily as the wife of American television and film composer Earle Hagen.
-
D.
Nancy Robertson
Nancy Robertson is the wife of prominent Canadian news anchor Lloyd Robertson.
-
E.
Kelly Roberts
Kelly Roberts is a businesswoman best known for owning and overseeing the historic Mission Inn Hotel & Spa in Riverside, California.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f9927c8190a006110c8b996e61 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b200e9108190bcdde5ba7938ba94 |
completed | April 19, 2026, 10:44 a.m. |
Created at: April 10, 2026, 10:22 a.m.