Triple
T2663597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanyang Technological University |
E54779
|
entity |
| Predicate | mainCampusSize |
P53
|
FINISHED |
| Object | approximately 200 hectares |
—
|
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: approximately 200 hectares | Statement: [Nanyang Technological University, mainCampusSize, approximately 200 hectares]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCampusSize Context triple: [Nanyang Technological University, mainCampusSize, approximately 200 hectares]
-
A.
campusSize
chosen
Indicates the physical extent or scale of a campus, typically measured in area or capacity.
-
B.
cityCampus
Indicates that a campus is located within or associated with a particular city.
-
C.
cityCampusServes
Indicates that a city campus provides services, resources, or support to a particular population, area, or institution.
-
D.
isLargestCampusOf
Indicates that one campus is the largest (by size, area, or capacity) among all campuses belonging to a particular institution or organization.
-
E.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
- 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_69ab49e028948190b97e01d73548b1d9 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd96b9f1c8190a8a9460ca88a9aaf |
completed | March 7, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69abd81768748190bd965f367cf6ef37 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.