Triple
T36736248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Wedding of Prince William and Catherine Middleton |
E907485
|
entity |
| Predicate | globalAudienceEstimate |
P3653
|
FINISHED |
| Object | 2 billion 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: 2 billion viewers | Statement: [Royal Wedding of Prince William and Catherine Middleton, globalAudienceEstimate, 2 billion viewers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: globalAudienceEstimate Context triple: [Royal Wedding of Prince William and Catherine Middleton, globalAudienceEstimate, 2 billion viewers]
-
A.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
B.
audienceSizeApproximate
chosen
Indicates an estimated or approximate number of people in the audience for an event or content.
-
C.
viewershipEstimateUnit
Indicates the unit of measurement used to express a viewership estimate (e.g., viewers, households, percentage).
-
D.
viewershipWorld
Indicates the extent or pattern of how an entity is watched or followed by audiences across the world.
-
E.
estimatedViewers
Indicates the number of viewers expected or predicted to watch a particular event, program, or content.
- 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_69f76e75aa6881909b844d00a3888ee5 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c9f5a8848190ba956ff27f44e396 |
completed | May 3, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69f7c8999a348190abc1895eaa6e036d |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:12 p.m.