Triple
T1454662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | pardon of Richard Nixon |
E31370
|
entity |
| Predicate | possibleEffect |
P23809
|
FINISHED |
| Object | contributed to Gerald Ford’s defeat in the 1976 presidential election |
—
|
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: contributed to Gerald Ford’s defeat in the 1976 presidential election | Statement: [pardon of Richard Nixon, possibleEffect, contributed to Gerald Ford’s defeat in the 1976 presidential election]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: possibleEffect Context triple: [pardon of Richard Nixon, possibleEffect, contributed to Gerald Ford’s defeat in the 1976 presidential election]
-
A.
notableEffect
chosen
Indicates that one entity has a significant impact, consequence, or influence on another entity or situation.
-
B.
primaryEffect
Indicates the main direct outcome or consequence that results from a given cause, action, or condition.
-
C.
involvedPhysicalEffect
Indicates that one entity participates in causing, experiencing, or mediating a physical effect on another entity or the environment.
-
D.
effectOfDeath
Indicates the causal impact or consequences that a death has on another entity, state, or process.
-
E.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
- 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_69a499171a28819085b993a3ac78e363 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c57fead881909a47188ce7312406 |
completed | March 1, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69a4c47cdbd0819092022344a2f4ad7b |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.