Triple
T12283343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chaco |
E292767
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Gran Chaco |
E136889
|
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 Chaco | Statement: [Chaco, partOf, Gran Chaco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gran Chaco Context triple: [Chaco, 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.
Pampa
Pampa is a jet trainer aircraft used by the Argentine Air Force, known for its role in pilot training and light attack missions.
-
E.
Pampa
Pampa was a pioneering 10th-century Kannada poet, celebrated as one of the “three gems” of classical Kannada literature and best known for his epic works like the Adipurana and Vikramarjuna Vijaya.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91d1ede3081908647595739c3e996 |
completed | April 10, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e70dec8819098199fbb54d888c1 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.