Triple
T36448674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | StudyLink |
E897946
|
entity |
| Predicate | policyFramework |
P25238
|
FINISHED |
| Object |
Student Loan Scheme Act
The Student Loan Scheme Act is New Zealand legislation that governs the provision, administration, and repayment of government-backed student loans for tertiary education.
|
E2183083
|
NE 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: Student Loan Scheme Act | Statement: [StudyLink, policyFramework, Student Loan Scheme Act]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Student Loan Scheme Act Triple: [StudyLink, policyFramework, Student Loan Scheme Act]
Generated description
The Student Loan Scheme Act is New Zealand legislation that governs the provision, administration, and repayment of government-backed student loans for tertiary education.
Provenance (5 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_69f76e5720b481908f8177ac24a7560b |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7bd8e3dac8190b55068ed07a077e5 |
completed | May 3, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a39c422b97c81908377120a025a66e8 |
completed | June 22, 2026, 11:24 p.m. |
| NEDg | Description generation | batch_6a39c49dc36c8190b6791483ee8a7e31 |
completed | June 22, 2026, 11:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a39c52475348190a171242f4714705d |
completed | June 22, 2026, 11:28 p.m. |
Created at: May 3, 2026, 4:10 p.m.