Triple
T10964484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Humphrey–Hawkins Act |
E259060
|
entity |
| Predicate | primaryPolicyArea |
P1876
|
FINISHED |
| Object | macroeconomic 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: macroeconomic policy | Statement: [Humphrey–Hawkins Act, primaryPolicyArea, macroeconomic policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryPolicyArea Context triple: [Humphrey–Hawkins Act, primaryPolicyArea, macroeconomic policy]
-
A.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
-
B.
emphasizesPolicyArea
Indicates that one entity gives particular importance or priority to a specific policy area in its actions, statements, or focus.
-
C.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
D.
policyFocus
chosen
Indicates that an entity (such as a person, organization, or document) is primarily concerned with, directed toward, or centered on a particular policy area or issue.
-
E.
policyAreaScope
Indicates the specific policy domain or thematic area to which an action, decision, or measure is relevant or applies.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d77148c4588190ad0f2b54ee4bab98 |
completed | April 9, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_69d72e8c27cc81908050590b7a04cafd |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.