Triple
T30580361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States terrorism history |
E778364
|
entity |
| Predicate | includesImpact |
P202544
|
FINISHED |
| Object | changes in U.S. immigration policy |
—
|
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: changes in U.S. immigration policy | Statement: [United States terrorism history, includesImpact, changes in U.S. immigration policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesImpact Context triple: [United States terrorism history, includesImpact, changes in U.S. immigration policy]
-
A.
recognizesImpactOn
Indicates that one entity acknowledges or understands the effect or consequences it has on another entity or situation.
-
B.
hasImpactScale
Indicates the degree or magnitude of impact that one entity or action has on another, typically expressed along a defined scale.
-
C.
exportImpact
Indicates the effect or consequences that an entity’s exports have on another entity, system, or context.
-
D.
canImpact
Indicates that one entity has the potential or ability to affect, influence, or cause a change in another entity.
-
E.
hasImpactFocus
Indicates that an entity is primarily concerned with or directed toward a particular type or area of impact.
- 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_69f224a04b248190b0ca443ec86207b8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a008ebb22408190a2293bced40a7e53 |
completed | May 10, 2026, 1:57 p.m. |
| PD | Predicate disambiguation | batch_6a008e8715dc8190ab23292605901bf4 |
completed | May 10, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_6a008eba6de8819083165152448f6071 |
completed | May 10, 2026, 1:57 p.m. |
Created at: April 29, 2026, 8:23 p.m.