Triple
T21230059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Christian O'Connell Breakfast Show (Absolute Radio) |
E523185
|
entity |
| Predicate | typicalSegments |
P9638
|
FINISHED |
| Object | comedy features |
—
|
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 features | Statement: [The Christian O'Connell Breakfast Show (Absolute Radio), typicalSegments, comedy features]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSegments Context triple: [The Christian O'Connell Breakfast Show (Absolute Radio), typicalSegments, comedy features]
-
A.
typicalSegmentType
chosen
Indicates that something is classified as belonging to a usual or characteristic type of segment within a broader structure or sequence.
-
B.
typicalTimes
Indicates the usual or characteristic times at which an event, activity, or condition typically occurs.
-
C.
notableSegmentType
Indicates that a particular segment or portion of something is classified as being of notable or special significance by its type.
-
D.
typicalSplit
Indicates that something is divided into parts or portions in the usual or most common way.
-
E.
typicalBreaks
Indicates that an entity commonly or characteristically causes a break, interruption, or failure in another entity or process.
- 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_69e0b512ad94819087942b2ed925185f |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e734af3d508190b3359d14496370b6 |
completed | April 21, 2026, 8:26 a.m. |
| PD | Predicate disambiguation | batch_69e5f60e1a888190ba75e2e900270a4e |
completed | April 20, 2026, 9:46 a.m. |
Created at: April 16, 2026, 3:45 p.m.