Triple
T27014538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Casey Ryback |
E680487
|
entity |
| Predicate | professionInCover |
P35550
|
FINISHED |
| Object | cook |
—
|
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: cook | Statement: [Casey Ryback, professionInCover, cook]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionInCover Context triple: [Casey Ryback, professionInCover, cook]
-
A.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
B.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
C.
memberProfession
chosen
Indicates that a member or individual holds or practices a particular profession or occupation.
-
D.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
E.
professionAttribute
Indicates that a specific attribute, quality, or characteristic is associated with a given profession.
- 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_69eeeb53939c8190bd431f32b060f01f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f657f653448190a945b4751af8507d |
completed | May 2, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69f6575ba12081909396036f78757a76 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 27, 2026, 7:05 a.m.