Triple
T15491594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sunday Love Songs |
E378698
|
entity |
| Predicate | hasRecurringSegments |
P58011
|
FINISHED |
| Object | listener dedications |
—
|
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: listener dedications | Statement: [Sunday Love Songs, hasRecurringSegments, listener dedications]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecurringSegments Context triple: [Sunday Love Songs, hasRecurringSegments, listener dedications]
-
A.
hasRecurringElement
Indicates that an entity includes an element that appears repeatedly or occurs multiple times within it.
-
B.
recurringSegmentOn
chosen
Indicates that one entity appears repeatedly as a regular segment or feature within another entity, such as a show, publication, or series.
-
C.
hasRepetition
Indicates that something occurs, appears, or is performed more than once, showing recurrence or repeated instances within a given context.
-
D.
recurringDuring
Indicates that an event or state happens repeatedly within the time span or context defined by another event or interval.
-
E.
hasMultipleSegments
Indicates that the referenced entity is composed of more than one distinct segment or section.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fac2af88190ac1d119e6b21dbe0 |
completed | April 16, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69ded2874b788190999158e0f043be21 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:48 a.m.