Triple
T9853646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ali Wong |
E239531
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ali Wong |
E239531
|
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: Ali Wong | Statement: [Ali Wong, name, Ali Wong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ali Wong Context triple: [Ali Wong, name, Ali Wong]
-
A.
Ali Wong
chosen
Ali Wong is an American stand-up comedian, actress, and writer known for her Netflix specials like "Baby Cobra" and her roles in film and television.
-
B.
Tiffany Haddish
Tiffany Haddish is an American stand-up comedian and actress known for her breakout role in "Girls Trip" and her energetic, unfiltered comedic style.
-
C.
Amber Ruffin
Amber Ruffin is an American comedian, writer, and television host best known for her work on "Late Night with Seth Meyers" and for creating and starring in "The Amber Ruffin Show."
-
D.
Natasha Leggero
Natasha Leggero is an American comedian, actress, and writer known for her sharp, satirical stand-up and numerous television and film comedy roles.
-
E.
Sarah Silverman
Sarah Silverman is an American stand-up comedian, actress, and writer known for her sharp, provocative humor and appearances in film and television.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb376d32c819089381cf6ed83629d |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5f21a04819099f23ede55ec3417 |
completed | April 5, 2026, 3:24 a.m. |
Created at: March 30, 2026, 8:34 p.m.