Triple
T13057463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grace Miguel |
E327614
|
entity |
| Predicate | mediaCoverageTopic |
P72922
|
FINISHED |
| Object | relationship with Usher |
—
|
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: relationship with Usher | Statement: [Grace Miguel, mediaCoverageTopic, relationship with Usher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaCoverageTopic Context triple: [Grace Miguel, mediaCoverageTopic, relationship with Usher]
-
A.
mediaCoverageFocus
chosen
Indicates the primary topic, subject, or aspect that media coverage is centered on or emphasizes.
-
B.
mediaCoverageAs
Indicates that one entity provides or receives media coverage in the role, capacity, or format specified by another entity.
-
C.
mediaCoverage
Indicates that one entity reports on, documents, or broadcasts information about another entity through news or media channels.
-
D.
mediaCoverageContext
Indicates the situational background, circumstances, or framing within which media coverage of an entity, event, or issue takes place.
-
E.
hasMediaCoverageTopic
Indicates that a piece of media coverage is about, focused on, or thematically related to a particular topic.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
Created at: April 9, 2026, 8:58 p.m.