Triple
T3242255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York State Senate districts |
E67987
|
entity |
| Predicate | dataSourceForDrawing |
P46377
|
FINISHED |
| Object | U.S. Census data |
—
|
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: U.S. Census data | Statement: [New York State Senate districts, dataSourceForDrawing, U.S. Census data]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dataSourceForDrawing Context triple: [New York State Senate districts, dataSourceForDrawing, U.S. Census data]
-
A.
dataSourceFor
chosen
Indicates that one entity serves as the origin or provider of data that is used or consumed by another entity.
-
B.
dataSourceForApportionment
Indicates that one entity serves as the source of data used to determine or calculate the apportionment of another entity.
-
C.
usesPointsForDraw
Indicates that an entity relies on or applies a points-based system to determine or execute a draw (such as a lottery, selection, or outcome).
-
D.
trainingDataSource
Indicates the origin or provider from which the training data for a model or system is obtained.
-
E.
usedInScoreGraphics
Indicates that something (such as a technique, element, or resource) is employed within the creation or presentation of score graphics.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaf17463481909447f6ab46016407 |
completed | March 8, 2026, 5:17 p.m. |
| PD | Predicate disambiguation | batch_69ada4159e0481908cbbdd750f5e08c7 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:08 p.m.