Triple

T10455715
Position Surface form Disambiguated ID Type / Status
Subject Two Moon E246546 entity
Predicate region P40 FINISHED
Object Northern Plains E9201 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: Northern Plains | Statement: [Two Moon, region, Northern Plains]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Northern Plains
Context triple: [Two Moon, region, Northern Plains]
  • A. Northern Great Plain
    The Northern Great Plain is a large, predominantly flat agricultural and economic region in eastern Hungary that includes major cities such as Debrecen.
  • B. Great Plains chosen
    The Great Plains is a vast, mostly flat grassland region in central North America known for its prairies, agriculture, and continental climate.
  • C. Southern Great Plain
    The Southern Great Plain is a large, predominantly flat agricultural region in southeastern Hungary known for its fertile lands and extensive farming.
  • D. Rio Grande Plains
    The Rio Grande Plains is a semi-arid, brush-covered physiographic region of southern Texas characterized by rolling plains, thorny shrublands, and ranching and oil activities.
  • E. Central Plains region
    The Central Plains region is a key economic and cultural heartland of China, centered around the middle and lower reaches of the Yellow River and encompassing major inland cities and agricultural areas.
  • 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_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fe48d15c8190bae0d4859e6cda5d completed April 7, 2026, 12:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69dbb6db3f2c81908a7cb28ca8e8ebc9 completed April 12, 2026, 3:14 p.m.
Created at: April 6, 2026, 12:18 p.m.