Triple
T34934035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liverpool, New South Wales |
E1007517
|
entity |
| Predicate | hasTAFE |
P52636
|
FINISHED |
| Object | TAFE NSW Liverpool |
—
|
NE NERFINISHED |
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: TAFE NSW Liverpool | Statement: [Liverpool, New South Wales, hasTAFE, TAFE NSW Liverpool]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTAFE Context triple: [Liverpool, New South Wales, hasTAFE, TAFE NSW Liverpool]
-
A.
hasFurtherEducationInstitution
Indicates that an entity is associated with, or has access to, a further education institution (such as a college or post-secondary training provider).
-
B.
hasUniversityOfAppliedSciences
Indicates that one entity is associated with, or hosts, a university of applied sciences as part of its structure or offerings.
-
C.
hasTechnicalCollege
chosen
Indicates that an entity possesses, includes, or is associated with a technical college as part of its structure or offerings.
-
D.
hasEducationIn
Indicates that an entity has received education, training, or formal study in a specified field, subject, or discipline.
-
E.
hasHigherEducationType
Indicates that one entity has a specified type or category of higher education associated with it.
- 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_69f76dc513fc819084a1ff52abbfa5bc |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782c98fa08190870b68de2c1ff26a |
completed | May 3, 2026, 5:15 p.m. |
| PD | Predicate disambiguation | batch_69f781020cc4819088c40cb8589504e4 |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 3, 2026, 4 p.m.