Triple
T25301643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allison Taylor |
E634360
|
entity |
| Predicate | isFictionalPresidentOf |
P155971
|
FINISHED |
| Object | United States of America |
—
|
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: United States of America | Statement: [Allison Taylor, isFictionalPresidentOf, United States of America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFictionalPresidentOf Context triple: [Allison Taylor, isFictionalPresidentOf, United States of America]
-
A.
hasFictionalUSPresident
chosen
Indicates that a work of fiction features a character who serves as President of the United States within its narrative.
-
B.
hasFictionalLeader
Indicates that an entity is led or governed by a leader who is a fictional character rather than a real person.
-
C.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
D.
hasFictionalSpokesperson
Indicates that an entity is represented or promoted by a spokesperson who is a fictional or imaginary character.
-
E.
favoritePresident
Indicates that one person regards a particular president as their preferred or most liked choice among all presidents.
- 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_69e75a972c6481909bc11710e8d30a6c |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f48fd8461c81908e461c9809bbfdbf |
completed | May 1, 2026, 11:34 a.m. |
| PD | Predicate disambiguation | batch_69f45d06d0388190b36ecde92013624a |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 21, 2026, 1:24 p.m.