Triple
T26920754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timothy P. Villagomez |
E677638
|
entity |
| Predicate | areaOfLawInvolved |
P6403
|
FINISHED |
| Object | public corruption 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: public corruption law | Statement: [Timothy P. Villagomez, areaOfLawInvolved, public corruption law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfLawInvolved Context triple: [Timothy P. Villagomez, areaOfLawInvolved, public corruption law]
-
A.
typeOfLaw
Indicates that one entity is a specific category or kind of law to which the other entity pertains.
-
B.
branchOfLaw
chosen
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
-
C.
legalCaseRelatedTo
Indicates that there is a relevant connection or association between a legal case and another entity, such as a person, organization, event, or legal matter.
-
D.
juridicalCategory
Indicates the legal classification or status under which an entity or relationship is formally recognized in a juridical system.
-
E.
legalMatters
Indicates that one entity is involved with, concerned about, or responsible for legal issues, processes, or obligations related to another entity or context.
- 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_69eee9bdebc48190ba90a12a63e09c73 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641dc8ff48190ab575d855616580c |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 6:07 a.m.