Triple

T19433104
Position Surface form Disambiguated ID Type / Status
Subject Ingeniero White E486163 entity
Predicate belongsTo P35 FINISHED
Object Argentine Pampas region NE NERFINISHED

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: Argentine Pampas region | Statement: [Ingeniero White, belongsTo, Argentine Pampas region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Argentine Pampas region
Context triple: [Ingeniero White, belongsTo, Argentine Pampas region]
  • A. Pampas
    Pampas is a town in central Peru that serves as the administrative and commercial hub of Tayacaja Province in the Huancavelica Region.
  • B. Pampas chosen
    The Pampas is a vast fertile lowland plain in South America, primarily in Argentina, known for its grasslands, agriculture, and cattle ranching.
  • C. 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.
  • 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 (2 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_69d8e8d7ad488190a3373045029b0f3b completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6335c2d7481909a44b45d95492e02 completed April 20, 2026, 2:08 p.m.
Created at: April 10, 2026, 1:37 p.m.