Triple
T90270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South America |
E1813
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Rio de Janeiro |
E6266
|
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: Rio de Janeiro | Statement: [South America, hasMajorCity, Rio de Janeiro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rio de Janeiro Context triple: [South America, hasMajorCity, Rio de Janeiro]
-
A.
Rio de Janeiro
chosen
Rio de Janeiro is a major Brazilian coastal city famed for its stunning beaches, dramatic landscape, Carnival festival, and iconic Christ the Redeemer statue.
-
B.
São Paulo
São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
-
C.
Buenos Aires
Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
-
D.
Santiago
Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
-
E.
Lisbon
Lisbon is the coastal capital city of Portugal, renowned for its historic architecture, hilly landscape, and role as a major cultural and economic center in Europe.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24f6c29888190890caa7872d63ac6 |
completed | Feb. 28, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a26c1cdad88190aae17fcf5554a674 |
completed | Feb. 28, 2026, 4:16 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.