Triple
T30999260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ronny Chieng |
E789887
|
entity |
| Predicate | hasNetflixSpecial |
P62152
|
FINISHED |
| Object | Asian Comedian Destroys America! |
—
|
LITERAL 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: Asian Comedian Destroys America! | Statement: [Ronny Chieng, hasNetflixSpecial, Asian Comedian Destroys America!]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNetflixSpecial Context triple: [Ronny Chieng, hasNetflixSpecial, Asian Comedian Destroys America!]
-
A.
hasTVSpecial
Indicates that an entity has an associated television special featuring it as the main subject or focus.
-
B.
hasTelevisionSpecialFormat
Indicates that a television special is presented or produced in a particular format or style.
-
C.
hasStandUpSpecial
chosen
Indicates that an entity has created, performed, or released a stand-up comedy special.
-
D.
includesSpecialEpisodes
Indicates that the subject collection or series contains one or more special, non-regular episodes.
-
E.
isTelevisionChristmasSpecial
Indicates that the subject is a television program specifically produced or designated as a Christmas-themed special.
- F. None of above.
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_69f224c65a348190baaed1c01a29900c |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fecb4d02f881909a9ee97ce98000d5 |
completed | May 9, 2026, 5:51 a.m. |
| PD | Predicate disambiguation | batch_69fec9846c1c8190b317f0711f0755db |
completed | May 9, 2026, 5:43 a.m. |
Created at: April 29, 2026, 8:56 p.m.