Triple
T418414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agricultural Adjustment Act of 1933 |
E8044
|
entity |
| Predicate | affectedCommodity |
P3553
|
FINISHED |
| Object | cotton |
—
|
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: cotton | Statement: [Agricultural Adjustment Act of 1933, affectedCommodity, cotton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedCommodity Context triple: [Agricultural Adjustment Act of 1933, affectedCommodity, cotton]
-
A.
commodity
chosen
Indicates that one entity is a tradable good or resource that is bought, sold, or exchanged in relation to another entity.
-
B.
affectedAgency
Indicates that one entity has an effect on, or causes a change in, the agency or capacity for action of another entity.
-
C.
affectedCountry
Indicates that a particular country is impacted or influenced by an event, action, or condition.
-
D.
complements
Indicates that one entity enhances, completes, or improves another by providing qualities or functions that fit well together.
-
E.
affectedCity
Indicates that a particular city is impacted or influenced by a specified event, action, or condition.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd1ca148190a66bd8c5aad867d5 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.