Triple

T1065427
Position Surface form Disambiguated ID Type / Status
Subject Royal Norwegian Order of Merit E23198 entity
Predicate hasAbbreviation P43 FINISHED
Object RNoM E123960 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: RNoM | Statement: [Royal Norwegian Order of Merit, hasAbbreviation, RNoM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RNoM
Context triple: [Royal Norwegian Order of Merit, hasAbbreviation, RNoM]
  • A. RNoM chosen
    RNoM is the post-nominal abbreviation used by recipients of the Royal Norwegian Order of Merit, a Norwegian order awarded for outstanding service in the interests of Norway.
  • B. NR
    NR is the standard abbreviation for National Rail, the collective network of passenger railway services in Great Britain.
  • C. REN
    REN is a blockchain-based project and protocol focused on enabling cross-chain liquidity and interoperability between different cryptocurrency networks.
  • D. RM
    RM is the currency symbol that was used to denote the German Reichsmark, the former official currency of Germany from 1924 to 1948.
  • E. Ren
    Ren is a central character in Margaret Atwood’s dystopian MaddAddam trilogy, known for her experiences as a sex worker and survivor in a bioengineered, post-apocalyptic world.
  • 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_69a493ee1f908190992b5f0d1b04459b completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b90f91248190ace1534a51b82bdd completed March 1, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac4c1d82c88190b418e2e2f050b563 completed March 7, 2026, 4:02 p.m.
Created at: March 1, 2026, 7:42 p.m.