Triple
T2164072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vicky Chun |
E46867
|
entity |
| Predicate | workSector |
P24248
|
FINISHED |
| Object | higher education |
—
|
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: higher education | Statement: [Vicky Chun, workSector, higher education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workSector Context triple: [Vicky Chun, workSector, higher education]
-
A.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
B.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
C.
workRelatedTo
Indicates a relationship where one entity’s work, tasks, or professional activities are connected, associated, or relevant to those of another entity.
-
D.
careerField
chosen
Indicates the professional domain or occupational area in which an entity works or specializes.
-
E.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
- 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_69a88a184cbc8190877791f6552c2484 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe8e1cfc81908adc0357ddfec701 |
completed | March 7, 2026, 5:58 a.m. |
| PD | Predicate disambiguation | batch_69abbd9c90408190b6b65498ca43ce26 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:45 p.m.