Triple
T163288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States presidential election |
E2955
|
entity |
| Predicate | termLimitForOffice |
P541
|
FINISHED |
| Object | two elected terms for president |
—
|
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: two elected terms for president | Statement: [United States presidential election, termLimitForOffice, two elected terms for president]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termLimitForOffice Context triple: [United States presidential election, termLimitForOffice, two elected terms for president]
-
A.
termLimitDefinedBy
chosen
Indicates that the duration or number of terms for holding a position is specified or constrained by a particular rule, law, or authority.
-
B.
termLength
Indicates the duration or period of time for which an agreement, position, or condition remains in effect.
-
C.
legislativeTermNumber
Indicates the ordinal number assigned to a specific legislative term within a sequence of legislative periods.
-
D.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
-
E.
legislativePeriod
Indicates the specific legislative term or session during which an action, event, or status is valid or took place.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a2585a1a6481908899ad51211950e8 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a2566392208190a538ea9aa1fac53e |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.