Triple
T34010284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quite Interesting Limited |
E872091
|
entity |
| Predicate | hasFormatSpecialism |
P57165
|
FINISHED |
| Object | comedy panel quiz shows |
—
|
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: comedy panel quiz shows | Statement: [Quite Interesting Limited, hasFormatSpecialism, comedy panel quiz shows]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFormatSpecialism Context triple: [Quite Interesting Limited, hasFormatSpecialism, comedy panel quiz shows]
-
A.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
B.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
C.
formatSpecialization
chosen
Indicates a relationship where one format is a specialized or more specific version of another, more general format.
-
D.
hasSpecialistStatus
Indicates that an entity holds a recognized specialist designation or status in a particular field, role, or context.
-
E.
isConsideredSpecialtyOf
Indicates that one field, practice, or area of expertise is regarded as a specialized branch or subset of another broader field.
- 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_69f349a08848819084b348d64c1879c3 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fdd07a34c08190982b8c61c2775cf6 |
completed | May 8, 2026, noon |
| PD | Predicate disambiguation | batch_69fdbd25c7908190b72fca8de7ce503f |
completed | May 8, 2026, 10:38 a.m. |
Created at: May 1, 2026, 1:51 a.m.