Triple
T17390392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ben Cafferty |
E422804
|
entity |
| Predicate | worksInInstitutionType |
P90962
|
FINISHED |
| Object | executive branch of the United States government |
—
|
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: executive branch of the United States government | Statement: [Ben Cafferty, worksInInstitutionType, executive branch of the United States government]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worksInInstitutionType Context triple: [Ben Cafferty, worksInInstitutionType, executive branch of the United States government]
-
A.
workInstitution
Indicates that an entity is employed by or works at a particular institution.
-
B.
inInstitution
Indicates that an entity is located within, belongs to, or is formally associated with a particular institution.
-
C.
typeOfInstitution
Indicates the specific kind or category of institution that an entity belongs to or is classified as.
-
D.
worksInOrganizationType
chosen
Indicates that an entity is employed by or performs work within an organization of a specified type (e.g., company, nonprofit, government agency).
-
E.
roleInInstitutions
Indicates that an entity holds or has held a specific role, position, or function within one or more institutions.
- 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_69d889d710288190bf0f4762801fefae |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43ab950d4819098d6a46f67c46191 |
completed | April 19, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69e3b02ac8688190a7182f1b2151d721 |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:45 a.m.