Triple
T26775658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Multistate Bar Examination |
E670112
|
entity |
| Predicate | coversAreaOfLaw |
P49402
|
FINISHED |
| Object | Constitutional 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: Constitutional Law | Statement: [Multistate Bar Examination, coversAreaOfLaw, Constitutional Law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversAreaOfLaw Context triple: [Multistate Bar Examination, coversAreaOfLaw, Constitutional Law]
-
A.
notableAreaOfLaw
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
-
B.
legalArea
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
C.
legalTopicCoverage
chosen
Indicates that one entity (such as a document, service, or resource) addresses, discusses, or is relevant to a particular legal topic or area of law.
-
D.
appliesToFieldOfLaw
Indicates that something is relevant or applicable to a particular field or branch of law.
-
E.
coversPolicyArea
Indicates that a policy, document, or initiative includes or addresses a particular policy area or topic within its scope.
- 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_69eeb31c925881909b597f6e40056d28 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b78f29481908cc8f390496dee97 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 27, 2026, 4:04 a.m.