Triple
T25737151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | County of Ventura government |
E645405
|
entity |
| Predicate | supervisorsTermLength |
P33746
|
FINISHED |
| Object | 4 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: 4 years | Statement: [County of Ventura government, supervisorsTermLength, 4 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supervisorsTermLength Context triple: [County of Ventura government, supervisorsTermLength, 4 years]
-
A.
termLength
Indicates the duration or period of time for which an agreement, position, or condition remains in effect.
-
B.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
C.
electsTermLength
Indicates the length of time for which an entity is elected to hold a particular position or office.
-
D.
termLengthCharacteristic
Indicates the specific duration-related property or feature associated with a term (such as its length or time span).
-
E.
termLengthNumber
chosen
Indicates the numerical value representing the duration or length of a specified term.
- 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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f661b58ac48190907b6c6e9ccc2c59 |
completed | May 2, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 21, 2026, 11:28 p.m.