Triple
T1885552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aeroparque Jorge Newbery |
E39955
|
entity |
| Predicate | locatedIn |
P40
|
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: [Aeroparque Jorge Newbery, locatedIn, Buenos Aires]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buenos Aires Context triple: [Aeroparque Jorge Newbery, locatedIn, 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.
Colonia Buenos Aires
Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
-
C.
Montevideo
Montevideo is the capital and largest city of Uruguay, serving as the country’s main political, economic, and cultural center.
-
D.
La Plata
La Plata is a municipality and town in Colombia known for its location in the western part of the Huila Department and its role as a regional agricultural and commercial center.
-
E.
La Plata
La Plata is the planned capital city of Argentina’s Buenos Aires Province, known for its distinctive diagonal street grid and cultural and educational institutions.
- 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_69a88633e4fc8190b7eb40463e048ec5 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb11eb2d0819088d67b1cfc772049 |
completed | March 7, 2026, 5:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae651c82588190b9f1461a7e135670 |
completed | March 9, 2026, 6:13 a.m. |
Created at: March 4, 2026, 7:34 p.m.