Triple
T31396580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Nixon’s 1952 Checkers speech |
E800879
|
entity |
| Predicate | estimatedViewers |
P177168
|
FINISHED |
| Object | approximately 60 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: approximately 60 million | Statement: [Richard Nixon’s 1952 Checkers speech, estimatedViewers, approximately 60 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedViewers Context triple: [Richard Nixon’s 1952 Checkers speech, estimatedViewers, approximately 60 million]
-
A.
viewershipEstimateUnit
Indicates the unit of measurement used to express a viewership estimate (e.g., viewers, households, percentage).
-
B.
viewershipNote
Indicates a note or annotation providing additional information or context about the viewership of something.
-
C.
viewershipImpact
Indicates how one entity’s viewership levels influence or change the audience size, engagement, or visibility of another entity.
-
D.
USViewers
Indicates the number or set of viewers located in the United States who watched or were exposed to a particular piece of content or event.
-
E.
effectOnViewers
Indicates the impact or influence that something has on those who observe or experience it.
- 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_69f224ea9998819086ae2e4f4f4091c8 |
completed | April 29, 2026, 3:34 p.m. |
| NER | Named-entity recognition | batch_69f6f85bfba48190aba95b40642a8ca7 |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f65fd1d08190b88e5e68ba268500 |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f854486c81909396d944a55e03ab |
completed | May 3, 2026, 7:25 a.m. |
Created at: April 29, 2026, 9:19 p.m.