Triple
T6788665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Justice for All with Judge Cristina Perez |
E155876
|
entity |
| Predicate | hasLegalTheme |
P69180
|
FINISHED |
| Object | civil 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: civil law | Statement: [Justice for All with Judge Cristina Perez, hasLegalTheme, civil law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalTheme Context triple: [Justice for All with Judge Cristina Perez, hasLegalTheme, civil law]
-
A.
hasLawTheme
chosen
Indicates that something is related to, concerned with, or thematically focused on law or legal matters.
-
B.
hasLegalSubject
Indicates that an entity serves as the legal subject (e.g., rights-holder or obligated party) in a legal relationship or context.
-
C.
hasLegalRight
Indicates that an entity possesses an officially recognized legal entitlement or permission to perform an action or hold a claim regarding another entity.
-
D.
hasLegalIssue
Indicates that an entity is involved in, associated with, or subject to a legal problem, dispute, or proceeding.
-
E.
hasLegalRelevanceIn
Indicates that something is legally significant, applicable, or has consequences within a specified legal context, case, or jurisdiction.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2aa2e0c8190b994261826ae001d |
completed | March 27, 2026, 6:55 p.m. |
| PD | Predicate disambiguation | batch_69c6d0979ce0819094678896da4e3169 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:14 p.m.