Triple
T5945169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Universidad de la República (Uruguay) |
E132261
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Salto |
E198492
|
NE 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: Salto | Statement: [Universidad de la República (Uruguay), hasCampusIn, Salto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salto Context triple: [Universidad de la República (Uruguay), hasCampusIn, Salto]
-
A.
Salto
chosen
Salto is a major city in northwestern Uruguay known for its hot springs, agriculture, and position as a key regional center near the Argentine border.
-
B.
Salto, Argentina
Salto, Argentina is a city in the Buenos Aires Province known for its agricultural economy and location along the Salto River.
-
C.
Moxos
Moxos is a historical region in the Bolivian lowlands, known for its indigenous cultures, extensive pre-Columbian earthworks, and rich Amazonian wetlands.
-
D.
Caranavi
Caranavi is a Bolivian town known as a key coffee-growing and agricultural hub in the Yungas region.
-
E.
Puerto Gaitán
Puerto Gaitán is a Colombian town and municipality known for its oil production, cattle ranching, and location in the eastern plains (Llanos) region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c00869d3308190af89b2453e0f7546 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0393a10448190b0960f4487e87448 |
completed | March 22, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c084fce481909c306d6eeb99066d |
completed | March 23, 2026, 4:24 a.m. |
Created at: March 22, 2026, 4:01 p.m.