Triple

T7603232
Position Surface form Disambiguated ID Type / Status
Subject Chaco Province E180035 entity
Predicate locatedInRegion 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 Province, locatedInRegion, Gran Chaco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gran Chaco
Context triple: [Chaco Province, locatedInRegion, 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 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.
  • 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 (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_69c69f3567008190ab01d2ca7b53584a completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f9fa633081909660f653f5b073cd completed March 27, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8684a98fc8190b3d0568f13ccd123 completed March 28, 2026, 11:46 p.m.
Created at: March 27, 2026, 3:54 p.m.