Triple
T9416768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ainsley Whitly |
E227041
|
entity |
| Predicate | hasProfessionField |
P35389
|
FINISHED |
| Object | journalism |
—
|
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: journalism | Statement: [Ainsley Whitly, hasProfessionField, journalism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionField Context triple: [Ainsley Whitly, hasProfessionField, journalism]
-
A.
hasNotableProfessionField
chosen
Indicates that an entity’s notable profession or occupation belongs to a particular professional field or domain.
-
B.
hasProfessionalSection
Indicates that an entity includes or is associated with a designated professional section, division, or category within its structure or content.
-
C.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
D.
hasProfessionalStatus
Indicates that an entity holds a particular professional standing, rank, or qualification within a field or occupation.
-
E.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd68cb4be08190a47f901a9703f9db |
completed | April 1, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69cca54c37f88190bddccf28e5fe5c84 |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:48 p.m.