Triple
T1681569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government of Argentina |
E36349
|
entity |
| Predicate | numberOfPresidentialTermsAllowed |
P10528
|
FINISHED |
| Object | 2 consecutive terms |
—
|
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 consecutive terms | Statement: [Government of Argentina, numberOfPresidentialTermsAllowed, 2 consecutive terms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPresidentialTermsAllowed Context triple: [Government of Argentina, numberOfPresidentialTermsAllowed, 2 consecutive terms]
-
A.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
-
B.
presidentialTerm
Indicates the period of time during which an individual officially serves as president of a country or organization.
-
C.
inaugurationFrequency
Indicates how often inaugurations occur within a given time period or context.
-
D.
allowsImmediatePresidentialReelection
chosen
Indicates that a system, rule, or provision permits a president to run for and assume a consecutive subsequent term without any intervening gap in office.
-
E.
setsTermOfOfficeFor
Indicates that one entity establishes or defines the duration and conditions of the term of office for 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aba644070c81908745b56d981fe273 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61b57a6881909373af287ef24799 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.