Triple
T28667094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miskatonic University |
E725611
|
entity |
| Predicate | hasFacultyMemberFictional |
P61558
|
FINISHED |
| Object | Professor Henry Armitage |
—
|
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: Professor Henry Armitage | Statement: [Miskatonic University, hasFacultyMemberFictional, Professor Henry Armitage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFacultyMemberFictional Context triple: [Miskatonic University, hasFacultyMemberFictional, Professor Henry Armitage]
-
A.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
B.
hasFictionalStaffMember
chosen
Indicates that an entity includes or employs a staff member who is a fictional character.
-
C.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
D.
hasFictionalEditor
Indicates that an entity is associated with a fictional editor character responsible for editing or overseeing its content within a narrative or fictional context.
-
E.
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.
- 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_69f01d85be388190b669a0e401e2f2c4 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
Created at: April 28, 2026, 5:01 a.m.