Triple
T16077485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michigan Senate |
E390013
|
entity |
| Predicate | termLimitsTotalYears |
P33746
|
FINISHED |
| Object | 8 years |
—
|
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: 8 years | Statement: [Michigan Senate, termLimitsTotalYears, 8 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termLimitsTotalYears Context triple: [Michigan Senate, termLimitsTotalYears, 8 years]
-
A.
termLimitDefinedBy
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.
termLengthNumber
chosen
Indicates the numerical value representing the duration or length of a specified term.
-
D.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
E.
electsTermLength
Indicates the length of time for which an entity is elected to hold a particular position or office.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e1827ad7c88190b867da511cbfb7fa |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:57 a.m.