Triple
T23937460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Our World |
E602676
|
entity |
| Predicate | approximateViewership |
P3846
|
FINISHED |
| Object | 400 million |
—
|
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: 400 million | Statement: [Our World, approximateViewership, 400 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateViewership Context triple: [Our World, approximateViewership, 400 million]
-
A.
viewershipEstimateUnit
Indicates the unit of measurement used to express a viewership estimate (e.g., viewers, households, percentage).
-
B.
approximateAudienceSize
chosen
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
C.
viewershipNote
Indicates a note or annotation providing additional information or context about the viewership of something.
-
D.
audienceSizeApproximate
Indicates an estimated or approximate number of people in the audience for an event or content.
-
E.
viewershipImpact
Indicates how one entity’s viewership levels influence or change the audience size, engagement, or visibility of another entity.
- 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_69e2953cf6e081909b8e25a10a52dddc |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1cfa1336c8190ac307a1b9497ba0b |
completed | April 29, 2026, 9:30 a.m. |
| PD | Predicate disambiguation | batch_69f1615518088190a206f54e2fdb14a3 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:05 p.m.