Triple

T7770148
Position Surface form Disambiguated ID Type / Status
Subject Per Ahlmark E179047 entity
Predicate name P16 FINISHED
Object Per Ahlmark E179047 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: Per Ahlmark | Statement: [Per Ahlmark, name, Per Ahlmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Per Ahlmark
Context triple: [Per Ahlmark, name, Per Ahlmark]
  • A. Per Ahlmark chosen
    Per Ahlmark was a Swedish politician, writer, and former leader of the Liberal People's Party who also served as Sweden’s Deputy Prime Minister in the 1970s.
  • B. Jörgen Persson
    Jörgen Persson is a Swedish cinematographer known for his work on numerous acclaimed films, including the 1998 adaptation of Les Misérables.
  • C. Charles Boberg
    Charles Boberg is a linguist and scholar of North American English dialects, particularly known for his work on regional variation and phonology.
  • D. Erik Sparre
    Erik Sparre was a prominent Swedish statesman and nobleman who rose to become one of the kingdom’s leading political figures in the late 16th and early 17th centuries.
  • E. Jan Uddenfeldt
    Jan Uddenfeldt is a Swedish engineer and telecommunications executive best known as a key technical leader at Ericsson and a pioneer in the development of mobile communication systems.
  • 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_69c69f30602c819082ab52cd4af5c592 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c70438ca2481909114b0c434717109 completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8deb127048190a89c08b7778df8a4 completed March 29, 2026, 8:11 a.m.
Created at: March 27, 2026, 4:11 p.m.