Triple
T7847472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 403(b) plan |
E181957
|
entity |
| Predicate | planSponsorType |
P2589
|
FINISHED |
| Object | public school districts |
—
|
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: public school districts | Statement: [403(b) plan, planSponsorType, public school districts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: planSponsorType Context triple: [403(b) plan, planSponsorType, public school districts]
-
A.
sponsoringOrganizationType
chosen
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
B.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
-
C.
planType
Indicates the specific category or kind of plan associated with an entity, such as its level, structure, or intended use.
-
D.
sponsors
Indicates that one entity provides financial or material support to another, often in exchange for association, promotion, or fulfillment of certain activities or goals.
-
E.
coSponsor
Indicates that an entity jointly supports, endorses, or backs an initiative, proposal, or activity together with one or more others.
- 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_69ca8285d6488190a95d4c02d7354b53 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb164105fc8190a60aaa27dd619d5a |
completed | March 31, 2026, 12:33 a.m. |
| PD | Predicate disambiguation | batch_69cae92180f88190ae3d44c3de7adc93 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:49 p.m.