Triple

T13046057
Position Surface form Disambiguated ID Type / Status
Subject Ecclesiastical province of Mercedes-Luján E327323 entity
Predicate region P40 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: [Ecclesiastical province of Mercedes-Luján, region, Pampas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pampas
Context triple: [Ecclesiastical province of Mercedes-Luján, region, 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. 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. 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.
  • 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_69d8076e64308190904fb5c93517c901 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d9805125e481908ed56f708de98a9e completed April 10, 2026, 10:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6d5fdd04c8190a86dbba1b81c8e6b completed May 3, 2026, 4:58 a.m.
Created at: April 9, 2026, 8:56 p.m.