Triple
T26284952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melvin Hicks |
E661110
|
entity |
| Predicate | caseClarified |
P56163
|
FINISHED |
| Object | burden-shifting framework in employment discrimination law |
—
|
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: burden-shifting framework in employment discrimination law | Statement: [Melvin Hicks, caseClarified, burden-shifting framework in employment discrimination law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caseClarified Context triple: [Melvin Hicks, caseClarified, burden-shifting framework in employment discrimination law]
-
A.
clarifiesThat
chosen
Indicates that one entity explains or makes another entity more understandable by removing ambiguity or confusion about it.
-
B.
caseReached
Indicates that a particular case, situation, or legal matter has arrived at, been brought before, or come under the consideration of a specified authority, stage, or entity.
-
C.
caseTypes
Indicates the types or categories of cases associated with or applicable to an entity or situation.
-
D.
caseLoad
Indicates the number or collection of cases, tasks, or matters currently assigned to or handled by an entity.
-
E.
interpretedInCase
Indicates that something is understood, analyzed, or given meaning within the context of a particular case or specific situational scenario.
- 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_69ee812bbd448190be4d7478b057990a |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60e76a034819088e757b72d480585 |
completed | May 2, 2026, 2:47 p.m. |
| PD | Predicate disambiguation | batch_69f5f7ff548c8190a23e98c5e66e0bc7 |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 10:04 p.m.