Triple
T17498183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugene Biscailuz |
E426121
|
entity |
| Predicate | publicOfficeCategory |
P14964
|
FINISHED |
| Object | elected law enforcement official |
—
|
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: elected law enforcement official | Statement: [Eugene Biscailuz, publicOfficeCategory, elected law enforcement official]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: publicOfficeCategory Context triple: [Eugene Biscailuz, publicOfficeCategory, elected law enforcement official]
-
A.
officeCategory
chosen
Indicates the classification or type of an office within a defined categorization scheme.
-
B.
governmentOffice
Indicates that an entity functions as an official administrative or governmental office responsible for carrying out public or state-related duties.
-
C.
officialCategoryIn
Indicates that an entity is formally classified within a specific official category or grouping in a given system or context.
-
D.
governmentCategory
Indicates the type or classification of a government associated with an entity.
-
E.
otherOffice
Indicates that one office is an alternative or additional office associated with the same organization, person, or entity as another office.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4520f6790819092c36e0e4ecc4cd3 |
completed | April 19, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f5fbcc8190a6ea9639bf5650da |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:48 a.m.