Triple
T4077691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. states |
E87403
|
entity |
| Predicate | haveCriminalLaw |
P52875
|
FINISHED |
| Object | state criminal code |
—
|
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: state criminal code | Statement: [U.S. states, haveCriminalLaw, state criminal code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveCriminalLaw Context triple: [U.S. states, haveCriminalLaw, state criminal code]
-
A.
isCriminalizedIn
Indicates that a specific behavior, action, or condition is prohibited and subject to legal penalties within a particular jurisdiction or legal system.
-
B.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
C.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
D.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
E.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
- F. None of above. chosen
Provenance (4 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_69aed9435cf48190ad1da737c962d19d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc4d348c8190a94724639830aca0 |
completed | March 9, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69aef9082c2081908474f082a49bebc8 |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aef9b34dec81909bbc3def9decc71a |
completed | March 9, 2026, 4:47 p.m. |
Created at: March 9, 2026, 3:39 p.m.