Triple
T34011512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Logan Mountstuart |
E872123
|
entity |
| Predicate | hasFictionalEducation |
P105210
|
FINISHED |
| Object | Oxford University |
—
|
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: Oxford University | Statement: [Logan Mountstuart, hasFictionalEducation, Oxford University]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalEducation Context triple: [Logan Mountstuart, hasFictionalEducation, Oxford University]
-
A.
hasFictionalSchool
Indicates that an entity is associated with or contains a school that exists only within a fictional or imaginary context.
-
B.
fictionalEducation
chosen
Indicates that one entity has an educational background, training, or schooling that exists only within a fictional or imaginary context relative to another entity.
-
C.
hasStudentFictional
Indicates that an entity has, is associated with, or is characterized by a student who is fictional rather than real.
-
D.
hasFictionalSpecialization
Indicates that an entity’s area of focus, expertise, or role is within a fictional or imaginative domain rather than a real-world specialization.
-
E.
hasEducationIn
Indicates that an entity has received education, training, or formal study in a specified field, subject, or discipline.
- 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_69f349a08848819084b348d64c1879c3 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff59b33a38819086cc9aa19b81748b |
completed | May 9, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69ff587758f88190a39c2164341dc554 |
completed | May 9, 2026, 3:53 p.m. |
Created at: May 1, 2026, 1:51 a.m.