Triple
T25277694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kinda Funny (podcast network) |
E633744
|
entity |
| Predicate | recurringGuest |
P127821
|
FINISHED |
| Object | Gary Whitta |
—
|
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: Gary Whitta | Statement: [Kinda Funny (podcast network), recurringGuest, Gary Whitta]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recurringGuest Context triple: [Kinda Funny (podcast network), recurringGuest, Gary Whitta]
-
A.
recurringGuestIn
chosen
Indicates that an entity appears repeatedly as a guest in a particular context, such as a show, event, or series.
-
B.
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.
-
C.
typicalGuests
Indicates the usual or most common guests associated with a particular host, place, or event.
-
D.
recurringCharacterInSeason
Indicates that a character appears repeatedly across multiple episodes within a specific season of a series.
-
E.
recurringCharacterIntroduced
Indicates that a character is introduced in a work as one who appears repeatedly across multiple episodes, issues, or installments.
- 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_69e75a9402fc81909362ca85277c06d9 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f497bc12b881908fe3386c66252bf6 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f49377411c8190b2188de444d76795 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 21, 2026, 1:18 p.m.