Triple
T46954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York State Senate |
E919
|
entity |
| Predicate | salaryType |
P2448
|
FINISHED |
| Object | paid legislators |
—
|
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: paid legislators | Statement: [New York State Senate, salaryType, paid legislators]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: salaryType Context triple: [New York State Senate, salaryType, paid legislators]
-
A.
typeOfContract
Indicates the specific kind or category of contractual agreement that applies between the related entities.
-
B.
standardType
Indicates that one entity is classified as the standard, canonical, or reference type for another entity or context.
-
C.
obligationType
Indicates the specific kind or category of duty, requirement, or commitment that applies within an obligation relationship.
-
D.
typeOfWork
Indicates the kind or category of work associated with or performed by an entity.
-
E.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
- F. None of above. chosen
Provenance (4 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24b1bf2c081908f20e13939b713ff |
completed | Feb. 28, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69a24abd07508190a83ffba5368c1c79 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24b1a42888190b56a5e457e11604f |
completed | Feb. 28, 2026, 1:55 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.