Triple
T12119771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leslie |
E288661
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Leslie Jones |
E704743
|
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: Leslie Jones | Statement: [Leslie, hasNotableBearer, Leslie Jones]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leslie Jones Context triple: [Leslie, hasNotableBearer, Leslie Jones]
-
A.
Leslie Jones
chosen
Leslie Jones is an American comedian and actress known for her work on "Saturday Night Live" and roles in films such as the 2016 "Ghostbusters" reboot.
-
B.
Leslie Jones
Leslie Jones is an American film editor known for her work on major Hollywood productions, including the feature film "Starsky & Hutch."
-
C.
Regina Hall
Regina Hall is an American actress and comedian known for her roles in films such as the Scary Movie series, Girls Trip, and numerous television comedies.
-
D.
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.
-
E.
Zazie Beetz
Zazie Beetz is a German-American actress known for her roles in the TV series "Atlanta" and films such as "Deadpool 2" and "Joker."
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91577a03c81909add7a5d7324a648 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f682397c819085a86a98e079660b |
completed | May 2, 2026, 1:05 p.m. |
Created at: April 8, 2026, 9:49 p.m.