Triple
T259856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Smoot–Hawley Tariff Act |
E5516
|
entity |
| Predicate | usedAsExampleOf |
P2506
|
FINISHED |
| Object | dangers of protectionism in economic literature |
—
|
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: dangers of protectionism in economic literature | Statement: [Smoot–Hawley Tariff Act, usedAsExampleOf, dangers of protectionism in economic literature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedAsExampleOf Context triple: [Smoot–Hawley Tariff Act, usedAsExampleOf, dangers of protectionism in economic literature]
-
A.
usedToExplain
Indicates that one entity serves as an explanation or clarification for another entity.
-
B.
alsoUsedIn
Indicates that something is additionally employed, applied, or present in another context, setting, or use case beyond the primary one.
-
C.
isUsedAs
chosen
Indicates that one entity serves a particular function, role, or purpose as another entity.
-
D.
usedFor
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
-
E.
usedWith
Indicates that one entity is typically or appropriately employed together with another entity in a combined or complementary use.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25f921c2881908821ca2c03815eae |
completed | Feb. 28, 2026, 3:22 a.m. |
| PD | Predicate disambiguation | batch_69a25b6b3ea88190bbd858999e42efae |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.