Triple
T391756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Kennedy |
E8895
|
entity |
| Predicate | hasEmployerType |
P2510
|
FINISHED |
| Object | Major League Baseball team |
—
|
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: Major League Baseball team | Statement: [Sam Kennedy, hasEmployerType, Major League Baseball team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmployerType Context triple: [Sam Kennedy, hasEmployerType, Major League Baseball team]
-
A.
employerType
chosen
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
B.
hasMajorEmployer
Indicates that an entity has a primary or most significant employer with which it is chiefly affiliated for work or occupation.
-
C.
hasTypeOfOrganization
Indicates that an entity is classified as belonging to a particular type or category of organization.
-
D.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
E.
formerEmployer
Indicates that one entity previously employed the other but no longer does so.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec7492288190bf33c9c869a0710f |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e96a8ca48190abbd8de9b02c115c |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.