Triple
T9196537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Erroll Garner Story |
E220727
|
entity |
| Predicate | subjectOccupationCovered |
P30232
|
FINISHED |
| Object | jazz pianist |
—
|
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: jazz pianist | Statement: [The Erroll Garner Story, subjectOccupationCovered, jazz pianist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectOccupationCovered Context triple: [The Erroll Garner Story, subjectOccupationCovered, jazz pianist]
-
A.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
coversOccupation
chosen
Indicates that one entity provides information about, includes, or pertains to another entity’s occupation or professional role.
-
C.
requiredOccupationOf
Indicates that one entity specifies the occupation or job role that is required or expected for another entity (such as a position, task, or qualification).
-
D.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
E.
workCovered
Indicates that one work or effort is included within the scope, protection, or responsibility defined by another entity or arrangement.
- 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_69ca83e7ba70819088b74866d9da2c30 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd87d6460819097234b5dd3f749b4 |
completed | April 1, 2026, 8:34 a.m. |
| PD | Predicate disambiguation | batch_69cc660af2408190ae06eb8326e1c64e |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:25 p.m.