Triple
T24265984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Black Lady Sketch Show |
E604838
|
entity |
| Predicate | hasRecurringGuestStar |
P127821
|
FINISHED |
| Object | Issa Rae |
—
|
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: Issa Rae | Statement: [A Black Lady Sketch Show, hasRecurringGuestStar, Issa Rae]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecurringGuestStar Context triple: [A Black Lady Sketch Show, hasRecurringGuestStar, Issa Rae]
-
A.
recurringGuestIn
chosen
Indicates that an entity appears repeatedly as a guest in a particular context, such as a show, event, or series.
-
B.
hasRecurringActor
Indicates that an actor appears repeatedly across multiple instances or episodes within a work or series.
-
C.
hasRecurringSeriesProtagonists
Indicates that a recurring series features one or more protagonists who appear repeatedly across its installments.
-
D.
isRecurringCharacter
Indicates that an entity appears repeatedly or regularly within a given narrative, series, or context rather than only once.
-
E.
guestStar
Indicates that one entity appears in a limited, special, or featured role within another entity’s production, event, or context, without being a regular or primary participant.
- 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_69e29544c29c8190b023606eafe5d36a |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28c6bfe68819084ec59235ae58ff1 |
completed | April 29, 2026, 10:55 p.m. |
| PD | Predicate disambiguation | batch_69f1c450aa508190bc9d372a5f6ee47a |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:06 a.m.