Triple
T24046595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Felicity Porter |
E595537
|
entity |
| Predicate | fictionalUniversityAttended |
P146006
|
FINISHED |
| Object | University of New York |
—
|
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: University of New York | Statement: [Felicity Porter, fictionalUniversityAttended, University of New York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalUniversityAttended Context triple: [Felicity Porter, fictionalUniversityAttended, University of New York]
-
A.
fictionalUniversityAffiliation
chosen
Indicates that an entity is affiliated with a university that exists only in a fictional or imaginary context.
-
B.
undergraduateInstitutionOf
Indicates that one entity is the institution where the other entity completed or pursued their undergraduate studies.
-
C.
hasFictionalSchool
Indicates that an entity is associated with or contains a school that exists only within a fictional or imaginary context.
-
D.
formerUniversityName
Indicates that an institution previously had a different official university name than the one it currently holds.
-
E.
setInFictionalUniversity
Indicates that the events or narrative take place within the setting of a fictional university.
- 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_69e288c06a908190899cad4531f32c9a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d9ca5a18819086da68b69eed8cc1 |
completed | April 29, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 10:16 p.m.