Triple

T5077596
Position Surface form Disambiguated ID Type / Status
Subject Río de la Plata region E114435 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: [Río de la Plata region, contains, Pampas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pampas
Context triple: [Río de la Plata region, 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_69bd443dbf908190a9401e9c2dc7bd7d completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd74f632788190ac4fd047e1a20485 completed March 20, 2026, 4:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb12718388190974df282ec2c6a11 completed March 21, 2026, 2:54 p.m.
Created at: March 20, 2026, 1:39 p.m.