Triple
T23820840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ellen Rosenblum |
E589231
|
entity |
| Predicate | areaOfPublicPolicy |
P1876
|
FINISHED |
| Object | consumer rights |
—
|
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: consumer rights | Statement: [Ellen Rosenblum, areaOfPublicPolicy, consumer rights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfPublicPolicy Context triple: [Ellen Rosenblum, areaOfPublicPolicy, consumer rights]
-
A.
policyAreaScope
Indicates the specific policy domain or thematic area to which an action, decision, or measure is relevant or applies.
-
B.
influencedPolicyArea
Indicates that one entity has affected, shaped, or guided the development, direction, or implementation of a particular policy area associated with another entity.
-
C.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
-
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.
policyTopic
Indicates that one entity is about, concerned with, or categorized under a particular policy-related subject or theme.
- 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_69e25d18619081909c7fb89d8926f14a |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c7af4d4481908095348fae9e54f4 |
completed | April 29, 2026, 8:56 a.m. |
| PD | Predicate disambiguation | batch_69f156036ad48190bc2ffdaf39218bcb |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:59 p.m.