Triple
T26755895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Nixon’s 1952 U.S. vice‑presidential campaign |
E674668
|
entity |
| Predicate | regionOfStrongSupportForTicket |
P31408
|
FINISHED |
| Object | Midwest |
—
|
NE NERFINISHED |
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: Midwest | Statement: [Richard Nixon’s 1952 U.S. vice‑presidential campaign, regionOfStrongSupportForTicket, Midwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionOfStrongSupportForTicket Context triple: [Richard Nixon’s 1952 U.S. vice‑presidential campaign, regionOfStrongSupportForTicket, Midwest]
-
A.
strongestSupportRegion
chosen
Indicates the region where an entity receives its highest level of support compared to all other regions.
-
B.
regionWithStrongBlocSupport
Indicates that a geographic region exhibits notably high or concentrated support for a particular political bloc or alliance.
-
C.
regionWithStrongReformSupport
Indicates that a region is characterized by notably high support for a particular reform or set of reforms.
-
D.
regionOfStrongSupportForLoser
Indicates a geographic area where the losing candidate or party nonetheless had particularly high levels of voter support.
-
E.
regionAtStake
Indicates that a specific geographic or political area is the subject of contention, risk, or negotiation within a given situation or interaction.
- 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_69eecda6e9dc81908452fab3ba17ed9b |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f74062b9388190b30546cf700a825c |
completed | May 3, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69f73c802b848190b61a416b7488bd96 |
completed | May 3, 2026, 12:16 p.m. |
Created at: April 27, 2026, 3:55 a.m.