Triple
T35333725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Grounds |
E1020394
|
entity |
| Predicate | campusSectorOf |
P113988
|
FINISHED |
| Object | University of Virginia |
—
|
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 Virginia | Statement: [North Grounds, campusSectorOf, University of Virginia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campusSectorOf Context triple: [North Grounds, campusSectorOf, University of Virginia]
-
A.
campusSector
chosen
Indicates that one location or facility is part of, or belongs to, a specific sector or zone within a campus.
-
B.
campusCategory
Indicates the classification or type assigned to a campus within a broader organizational or geographic system.
-
C.
campusArea
Indicates that one entity is the physical area or spatial extent of a campus associated with another entity.
-
D.
universitySectionOf
Indicates that one entity is a specific section, division, or part within a larger university.
-
E.
campus
Indicates that an entity is located on, associated with, or taking place within a particular campus.
- 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_69f76deacf4481908e7735a5a7715b0a |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79533b88c8190934ec4cb21770e24 |
completed | May 3, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69f79104f5b48190a496cdffde8472da |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.