Triple
T15214708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colegrove v. Green |
E363606
|
entity |
| Predicate | isRelatedAreaOfLaw |
P113669
|
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: [Colegrove v. Green, isRelatedAreaOfLaw, constitutional law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isRelatedAreaOfLaw Context triple: [Colegrove v. Green, isRelatedAreaOfLaw, constitutional law]
-
A.
appliesToFieldOfLaw
chosen
Indicates that something is relevant or applicable to a particular field or branch of law.
-
B.
branchOfLaw
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
-
C.
notableAreaOfLaw
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
-
D.
hasLegalRelevanceIn
Indicates that something is legally significant, applicable, or has consequences within a specified legal context, case, or jurisdiction.
-
E.
hasJurisprudenceField
Indicates that an entity’s work, expertise, or classification pertains to a specific field or branch of jurisprudence (legal theory or law).
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076e4348819091fa91c1562e7c5c |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:11 a.m.