Triple
T8240346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ezeiza |
E192518
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Gran Buenos Aires |
E288011
|
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: Gran Buenos Aires | Statement: [Ezeiza, partOf, Gran Buenos Aires]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gran Buenos Aires Context triple: [Ezeiza, partOf, Gran Buenos Aires]
-
A.
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.
-
B.
Colonia Buenos Aires
Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
-
C.
Mar del Plata
Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
-
D.
Greater Buenos Aires
chosen
Greater Buenos Aires is the vast, densely populated metropolitan region surrounding Argentina’s capital city, encompassing Buenos Aires and its many suburban municipalities.
-
E.
Count of Buenos Aires
Count of Buenos Aires is a Spanish noble title historically associated with Santiago de Liniers, a key colonial-era figure in the defense and governance of Buenos Aires.
- 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_69ca82dc8f148190a2c75a98501a7b91 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb783e13648190abf34eb8c244ea17 |
completed | March 31, 2026, 7:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d160f6855c81909ae0f3f1c041f600 |
completed | April 4, 2026, 7:05 p.m. |
Created at: March 30, 2026, 5:47 p.m.