Triple
T1445814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Educational Opportunities Section |
E31173
|
entity |
| Predicate | enforcesLawArea |
P2167
|
FINISHED |
| Object | discrimination based on race in education |
—
|
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: discrimination based on race in education | Statement: [Educational Opportunities Section, enforcesLawArea, discrimination based on race in education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: enforcesLawArea Context triple: [Educational Opportunities Section, enforcesLawArea, discrimination based on race in education]
-
A.
legalArea
chosen
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
B.
policePrecinct
Indicates that a specified location, building, or area functions as or is designated as a police precinct.
-
C.
enforcementAgency
Indicates that one entity serves as the authority responsible for enforcing laws, rules, or regulations related to another entity.
-
D.
enforcedLaw
Indicates that an authority actively applies or upholds a specific law to regulate behavior or resolve situations.
-
E.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with 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_69a4991633388190a4d61b5a98aa407a |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c55714588190a95b4f677c21cbaa |
completed | March 1, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69a4c47a840c819083307a65c027a19e |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.