Triple
T25484493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samantha MacKenzie |
E638662
|
entity |
| Predicate | fictionalFatherOccupation |
P34569
|
FINISHED |
| Object | President of the United States |
—
|
NE NERFINISHED |
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: President of the United States | Statement: [Samantha MacKenzie, fictionalFatherOccupation, President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalFatherOccupation Context triple: [Samantha MacKenzie, fictionalFatherOccupation, President of the United States]
-
A.
fictionalFatherCharacterPortrayedBy
Indicates that a fictional father character is portrayed or acted by a specific performer or actor.
-
B.
fictionalOccupation
chosen
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
C.
fictionalGrandfather
Indicates that one entity is the fictional grandfather (a grandfather character within a story or fictional universe) of another entity.
-
D.
fatherOccupation
Indicates the type of job or profession held by a person's father.
-
E.
hasFictionalFather
Indicates that one entity is the fictional father of another entity.
- 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_69e75dbabeac8190bab30628f8b799d4 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f62d89b89c8190afb372a8172111e7 |
completed | May 2, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69f62c1379f08190836c3e02b0c892df |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 21, 2026, 2:32 p.m.