Triple
T1681693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Buenos Aires |
E36351
|
entity |
| Predicate | rankingRegional |
P13048
|
FINISHED |
| Object | among top universities in Latin America |
—
|
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: among top universities in Latin America | Statement: [University of Buenos Aires, rankingRegional, among top universities in Latin America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingRegional Context triple: [University of Buenos Aires, rankingRegional, among top universities in Latin America]
-
A.
regionRankContext
chosen
Indicates the relative ranking or position of something within a specific geographic or regional context.
-
B.
areaRank
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
-
C.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
D.
countryRankContext
Indicates the relative position or ranking of a country within a specified contextual framework (such as economic, political, or performance-based criteria).
-
E.
nationalRank
Indicates the position or standing of an entity within a ranking system at the national level.
- 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aba644070c81908745b56d981fe273 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61b57a6881909373af287ef24799 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.