Triple
T7319488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rafael de Sobremonte |
E168500
|
entity |
| Predicate | associatedWithPlace |
P2830
|
FINISHED |
| Object | Buenos Aires |
E5323
|
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: Buenos Aires | Statement: [Rafael de Sobremonte, associatedWithPlace, Buenos Aires]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buenos Aires Context triple: [Rafael de Sobremonte, associatedWithPlace, Buenos Aires]
-
A.
Buenos Aires
chosen
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.
-
B.
Mar del Plata
Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
-
C.
Colonia Buenos Aires
Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
-
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.
Montevideo
Montevideo is the capital and largest city of Uruguay, serving as the country’s main political, economic, and cultural center.
- 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_69c68a5251508190ad68df4151cfeb04 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ef1a7a3c81909504eb711056f302 |
completed | March 27, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802ae19d88190a2f7997a6f3dfb1e |
completed | March 28, 2026, 4:32 p.m. |
Created at: March 27, 2026, 3:02 p.m.