Triple
T15987706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luke and Laura wedding |
E387738
|
entity |
| Predicate | viewershipEstimateUnit |
P121192
|
FINISHED |
| Object | viewers |
—
|
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: viewers | Statement: [Luke and Laura wedding, viewershipEstimateUnit, viewers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: viewershipEstimateUnit Context triple: [Luke and Laura wedding, viewershipEstimateUnit, viewers]
-
A.
viewershipNote
Indicates a note or annotation providing additional information or context about the viewership of something.
-
B.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
C.
viewershipImpact
Indicates how one entity’s viewership levels influence or change the audience size, engagement, or visibility of another entity.
-
D.
audienceSizeApproximate
Indicates an estimated or approximate number of people in the audience for an event or content.
-
E.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
- F. None of above. chosen
Provenance (4 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4e871c819082d7b1c1eaf5b4fe |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d9d8e881909b559a3e3ca21d24 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e17d48cc9c8190b03fd07ae2e9dfd8 |
completed | April 17, 2026, 12:22 a.m. |
Created at: April 10, 2026, 4:54 a.m.