Triple

T10642987
Position Surface form Disambiguated ID Type / Status
Subject Hill Mari E250768 entity
Predicate languageCodeISO639-3 P208 FINISHED
Object mrj
mrj is the ISO 639-3 code for Hill Mari, a Uralic language spoken by the Mari people primarily in Russia’s Mari El Republic.
 E877900 NE FINISHED

How this triple was built (4 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: mrj | Statement: [Hill Mari, languageCodeISO639-3, mrj]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: mrj
Context triple: [Hill Mari, languageCodeISO639-3, mrj]
  • A. MR
    MR is a Belgian French-speaking liberal political party that participated as one of the partners in the federal Vivaldi coalition government led by Alexander De Croo.
  • B. MR
    MR is the official vehicle registration code used on license plates for the city of Marburg in the German state of Hesse.
  • C. MJ
    MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
  • D. MJ
    MJ is the widely used nickname for Michael Jordan, the legendary American basketball player often regarded as the greatest in NBA history.
  • E. MJ
    MJ is a reimagined version of the Mary Jane Watson character who appears as Peter Parker’s sharp, observant classmate and love interest in the Marvel Cinematic Universe Spider-Man films.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: mrj
Triple: [Hill Mari, languageCodeISO639-3, mrj]
Generated description
mrj is the ISO 639-3 code for Hill Mari, a Uralic language spoken by the Mari people primarily in Russia’s Mari El Republic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: mrj
Target entity description: mrj is the ISO 639-3 code for Hill Mari, a Uralic language spoken by the Mari people primarily in Russia’s Mari El Republic.
  • A. MR
    MR is a Belgian French-speaking liberal political party that participated as one of the partners in the federal Vivaldi coalition government led by Alexander De Croo.
  • B. MR
    MR is the official vehicle registration code used on license plates for the city of Marburg in the German state of Hesse.
  • C. MJ
    MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
  • D. MJ
    MJ is the widely used nickname for Michael Jordan, the legendary American basketball player often regarded as the greatest in NBA history.
  • E. MJ
    MJ is a reimagined version of the Mary Jane Watson character who appears as Peter Parker’s sharp, observant classmate and love interest in the Marvel Cinematic Universe Spider-Man films.
  • F. None of above. chosen

Provenance (5 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_69d6aa5a4c4881908f39be6efe5981e5 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfcf65fc81909a0c86daefaab1ab completed April 8, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a4555e48190be39c0a7698b4282 completed April 10, 2026, 10:31 p.m.
NEDg Description generation batch_69d97cc07100819088683a0d79b2baa0 completed April 10, 2026, 10:42 p.m.
NED2 Entity disambiguation (via description) batch_69d97e0cda0c8190af5013b971b2ad3c completed April 10, 2026, 10:47 p.m.
Created at: April 8, 2026, 9:05 p.m.