Triple
T8911178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Lewis |
E212184
|
entity |
| Predicate | employeeStatus |
P72402
|
FINISHED |
| Object | partners |
—
|
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: partners | Statement: [John Lewis, employeeStatus, partners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employeeStatus Context triple: [John Lewis, employeeStatus, partners]
-
A.
careerStatus
Indicates the current stage, position, or condition of an entity within its professional or occupational life.
-
B.
departmentStatus
Indicates the current operational or organizational state assigned to a department.
-
C.
stateOfEmployment
chosen
Indicates that one entity’s employment status or condition is defined in relation to another entity (such as an employer, position, or employment situation).
-
D.
designationStatus
Indicates the current official classification or standing assigned to an entity within a defined system or process.
-
E.
employeeRight
Indicates that an employee holds a specific right, entitlement, or privilege within an employment or workplace context.
- 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6523b9348190a7cefac9e73e2004 |
completed | April 1, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69cc5ecf55248190a29f00fbf99f13c4 |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:55 p.m.