Triple
T23362158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wholly Moses! |
E593211
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Laraine Newman |
—
|
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: Laraine Newman | Statement: [Wholly Moses!, starring, Laraine Newman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laraine Newman Context triple: [Wholly Moses!, starring, Laraine Newman]
-
A.
Laraine Newman
chosen
Laraine Newman is an American comedian and actress best known as one of the original cast members of Saturday Night Live in the 1970s.
-
B.
Kathy Najimy
Kathy Najimy is an American actress and comedian best known for her roles in films like "Hocus Pocus" and "Sister Act" and for her extensive voice work in animation.
-
C.
Pam Dawber
Pam Dawber is an American actress best known for starring opposite Robin Williams in the television sitcom "Mork & Mindy."
-
D.
Fran Drescher
Fran Drescher is an American actress and comedian best known for starring in the 1990s sitcom "The Nanny" and her distinctive nasal voice and comedic style.
-
E.
Shelley Long
Shelley Long is an American actress best known for her Emmy-winning role as Diane Chambers on the television sitcom "Cheers."
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a0a8669c819098b88ae6712e3f88 |
completed | April 29, 2026, 6:09 a.m. |
Created at: April 17, 2026, 5:30 p.m.