Triple

T19996558
Position Surface form Disambiguated ID Type / Status
Subject Puerto Rawson E494208 entity
Predicate partOf P40 FINISHED
Object Rawson Department 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: Rawson Department | Statement: [Puerto Rawson, partOf, Rawson Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rawson Department
Context triple: [Puerto Rawson, partOf, Rawson Department]
  • A. Rawson Department chosen
    Rawson Department is an administrative division in Chubut Province, Argentina, that includes the city of Trelew among its main localities.
  • B. Rawson Department
    Rawson Department is an administrative division within Argentina’s San Juan Province, known for its mix of urban and agricultural areas near the provincial capital.
  • C. Kadey Department
    Kadey Department is an administrative division in eastern Cameroon known for its largely rural communities, forested landscapes, and proximity to the borders with the Central African Republic.
  • D. Gaiman Department
    Gaiman Department is an administrative division in Chubut Province, Argentina, known for its Welsh heritage and small rural towns.
  • E. Mason District
    Mason District is a magisterial district in Fairfax County, Virginia, encompassing several communities in the inner Washington, D.C. suburbs.
  • 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_69da626b2d748190886981ea90c8b2ea completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e65fe549108190947a4d1a587c08f8 completed April 20, 2026, 5:18 p.m.
Created at: April 11, 2026, 3:32 p.m.