Triple
T8609771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ANEP |
E203887
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Montevideo |
E47651
|
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: Montevideo | Statement: [ANEP, headquartersLocation, Montevideo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montevideo Context triple: [ANEP, headquartersLocation, Montevideo]
-
A.
Montevideo
chosen
Montevideo is the capital and largest city of Uruguay, serving as the country’s main political, economic, and cultural center.
-
B.
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.
-
C.
Ciudad del Este
Ciudad del Este is a major commercial city in eastern Paraguay, known as a busy border trading hub near the tri-border area with Brazil and Argentina.
-
D.
Bahía Blanca
Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
-
E.
Caxias do Sul
Caxias do Sul is a major city in southern Brazil known for its strong European immigrant heritage, particularly German and Italian influences, and its significant industrial and wine-producing sectors.
- 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_69ca832c23e4819095a9f3eea4a21828 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46ed77588190a872d22d9d1f7429 |
completed | March 31, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf889f91288190b052c4a41359d743 |
completed | April 3, 2026, 9:30 a.m. |
Created at: March 30, 2026, 6:25 p.m.