Triple
T1191240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vienna University of Economics and Business |
E25364
|
entity |
| Predicate | campusOpeningYear |
P26198
|
FINISHED |
| Object | 2013 |
—
|
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: 2013 | Statement: [Vienna University of Economics and Business, campusOpeningYear, 2013]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campusOpeningYear Context triple: [Vienna University of Economics and Business, campusOpeningYear, 2013]
-
A.
campusType
Indicates the classification or category of a campus based on its type (e.g., main, satellite, urban, rural).
-
B.
campus
Indicates that an entity is located on, associated with, or taking place within a particular campus.
-
C.
campusSize
Indicates the physical extent or scale of a campus, typically measured in area or capacity.
-
D.
universityLocatedIn
Indicates that a university is situated within or associated with a specific geographic location or administrative region.
-
E.
cityCampus
Indicates that a campus is located within or associated with a particular city.
- 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd74e2c08190b4a48425f94addaa |
completed | March 1, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5bacc481909e8dfd5215e4711a |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bd0ab5f88190bb583fc63b4cc150 |
completed | March 1, 2026, 10:26 p.m. |
Created at: March 1, 2026, 7:45 p.m.