Triple
T160163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legislative Analyst’s Office |
E3264
|
entity |
| Predicate | topicOfAnalysis |
P1945
|
FINISHED |
| Object | California state budget proposals |
—
|
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: California state budget proposals | Statement: [Legislative Analyst’s Office, topicOfAnalysis, California state budget proposals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: topicOfAnalysis Context triple: [Legislative Analyst’s Office, topicOfAnalysis, California state budget proposals]
-
A.
analyzes
Indicates that one entity systematically examines or evaluates another entity to understand its nature, structure, or components.
-
B.
subjectMatter
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
C.
academicFocus
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
D.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
E.
studiedBy
chosen
Indicates that a subject (such as a field, topic, or object) is examined, researched, or learned by an agent (such as a person or group).
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25856d934819095460b2ea566eb6b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256623704819089d9eeefe05858ce |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.