Triple
T3898336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | America |
E90426
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Pampas |
E25063
|
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: Pampas | Statement: [America, contains, Pampas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pampas Context triple: [America, contains, Pampas]
-
A.
Pampas
chosen
The Pampas is a vast fertile lowland plain in South America, primarily in Argentina, known for its grasslands, agriculture, and cattle ranching.
-
B.
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.
-
C.
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.
-
D.
Patagonian steppe
The Patagonian steppe is a vast, windswept cold desert and grassland region in southern Argentina, characterized by sparse vegetation, arid climate, and extensive sheep ranching.
-
E.
Gran Chaco
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.
- 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecd48b208190afaa62975805d087 |
completed | March 9, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51ca0f72c819084726f631a947f8c |
completed | March 14, 2026, 8:30 a.m. |
Created at: March 9, 2026, 3:21 p.m.