Triple
T22706021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alto Paraguay Department |
E561454
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Gran Chaco |
—
|
NE NERFINISHED |
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 Chaco | Statement: [Alto Paraguay Department, partOf, Gran Chaco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gran Chaco Context triple: [Alto Paraguay Department, partOf, Gran Chaco]
-
A.
Gran Chaco
chosen
The Gran Chaco is a vast, sparsely populated lowland plain in central South America, known for its hot, semi-arid climate and dry forests spanning parts of Argentina, Paraguay, Bolivia, and Brazil.
-
B.
Misiones rainforest
The Misiones rainforest is a subtropical forest in northeastern Argentina renowned for its rich biodiversity, red-soil landscapes, and iconic Iguazú Falls.
-
C.
Pampas
The Pampas is a vast fertile lowland plain in South America, primarily in Argentina, known for its grasslands, agriculture, and cattle ranching.
-
D.
Pampas
Pampas is a town in central Peru that serves as the administrative and commercial hub of Tayacaja Province in the Huancavelica Region.
-
E.
Pampa
Pampa is a small city in the Texas Panhandle known historically for its role in the oil and gas industry and as a regional service and trade center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2454f1348819088d83f420925a5c1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178ceba6c8190a538366a8e4648de |
completed | April 29, 2026, 3:19 a.m. |
Created at: April 17, 2026, 3:17 p.m.