Triple
T1700045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King’s College Circle |
E36746
|
entity |
| Predicate | campusAreaType |
P31311
|
FINISHED |
| Object | quadrangle lawn |
—
|
LITERAL FINISHED |
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: quadrangle lawn | Statement: [King’s College Circle, campusAreaType, quadrangle lawn]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campusAreaType Context triple: [King’s College Circle, campusAreaType, quadrangle lawn]
-
A.
campusArea
Indicates that one entity is the physical area or spatial extent of a campus associated with another entity.
-
B.
campusType
Indicates the classification or category of a campus based on its type (e.g., main, satellite, urban, rural).
-
C.
campusFacilityType
Indicates the specific kind of facility a campus location is classified as (e.g., library, laboratory, residence hall).
-
D.
cityCampus
Indicates that a campus is located within or associated with a particular city.
-
E.
cityCampusServes
Indicates that a city campus provides services, resources, or support to a particular population, area, or institution.
- F. None of above. chosen
Provenance (4 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_69a886163dec8190859c514232a37a05 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf169da888190b3aa334752f1952b |
completed | March 6, 2026, 3:23 p.m. |
| PD | Predicate disambiguation | batch_69aa61b8ce348190b46154af0b041ff0 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69aaf16865488190a76577b36760dc7a |
completed | March 6, 2026, 3:23 p.m. |
Created at: March 4, 2026, 7:30 p.m.