Triple
T85037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agricultural Adjustment Administration |
E1710
|
entity |
| Predicate | subsidyType |
P936
|
FINISHED |
| Object | payments for leaving land fallow |
—
|
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: payments for leaving land fallow | Statement: [Agricultural Adjustment Administration, subsidyType, payments for leaving land fallow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subsidyType Context triple: [Agricultural Adjustment Administration, subsidyType, payments for leaving land fallow]
-
A.
scholarshipType
Indicates the specific category or kind of scholarship associated with an entity.
-
B.
typeOfSupport
chosen
Indicates the kind or category of assistance, help, or backing provided in a given context.
-
C.
settlementType
Indicates the specific kind or category of human settlement an entity represents, such as a city, village, town, or hamlet.
-
D.
obligationType
Indicates the specific kind or category of duty, requirement, or commitment that applies within an obligation relationship.
-
E.
tuitionFeeType
Indicates the category or structure of tuition fees that applies to an entity (such as full-time, part-time, in-state, out-of-state, or other fee types).
- 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_69a24c8150408190910a693eb51c1f71 |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a250e401288190ba12322c9c5f07c9 |
completed | Feb. 28, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69a24eb59e808190811c20518f39b1cc |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.