Triple
T454335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Employees’ Compensation Appeals Board |
E7199
|
entity |
| Predicate | employerScope |
P6976
|
FINISHED |
| Object | federal employees |
—
|
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: federal employees | Statement: [Employees’ Compensation Appeals Board, employerScope, federal employees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerScope Context triple: [Employees’ Compensation Appeals Board, employerScope, federal employees]
-
A.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
B.
organizationalScope
chosen
Indicates the range or extent of responsibility, authority, or applicability that an action, policy, or relationship has within an organization or its sub-units.
-
C.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
D.
recruitingScope
Indicates the extent or boundaries within which recruiting activities are conducted or targeted.
-
E.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef87cc7c8190a0fec933457821e2 |
completed | Feb. 28, 2026, 1:37 p.m. |
| PD | Predicate disambiguation | batch_69a2ede4de008190b5a6c159e741522e |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.