Triple

T26233759
Position Surface form Disambiguated ID Type / Status
Subject Victorian TAFE system E656109 entity
Predicate usesFundingSystem P59 FINISHED
Object Skills First program
The Skills First program is a Victorian government initiative that funds and supports vocational education and training to improve access to quality, industry-relevant courses for students.
 E1713492 NE FINISHED

How this triple was built (3 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: Skills First program | Statement: [Victorian TAFE system, usesFundingSystem, Skills First program]
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: Skills First program
Triple: [Victorian TAFE system, usesFundingSystem, Skills First program]
Generated description
The Skills First program is a Victorian government initiative that funds and supports vocational education and training to improve access to quality, industry-relevant courses for students.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: usesFundingSystem
Context triple: [Victorian TAFE system, usesFundingSystem, Skills First program]
  • A. usedFundingFrom
    Indicates that one entity utilized financial resources that originated from another entity.
  • B. usedFund
    Indicates that one entity expended or applied a particular fund or financial resource for some purpose.
  • C. includesFundingFor
    Indicates that one entity’s financial resources or budget allocation explicitly cover or provide funding for another entity, project, or activity.
  • D. fundingModel chosen
    Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
  • E. usedFundingMethods
    Indicates that an entity employed specific funding methods to finance an activity, project, or operation.
  • F. None of above.

Provenance (6 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_69ee5b4b8b408190993da38c0067cc8d completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f612607c388190ab61d1ac7d18e08d completed May 2, 2026, 3:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11859a1d148190adee8ee0275bcc56 completed May 23, 2026, 10:46 a.m.
NEDg Description generation batch_6a11862050608190bf26431a0fb90b07 completed May 23, 2026, 10:49 a.m.
NED2 Entity disambiguation (via description) batch_6a1186c04c2c8190a5e70c9d9a5cbeb8 completed May 23, 2026, 10:51 a.m.
PD Predicate disambiguation batch_69f611a9272881909093360472be832c completed May 2, 2026, 3 p.m.
Created at: April 26, 2026, 9 p.m.