Triple

T2183941
Position Surface form Disambiguated ID Type / Status
Subject Lund University E49107 entity
Predicate hasRector P325 FINISHED
Object Erik Renström
Erik Renström is a Swedish academic and professor who serves as the rector (vice-chancellor) of Lund University.
E243319 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: Erik Renström | Statement: [Lund University, hasRector, Erik Renström]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Erik Renström
Context triple: [Lund University, hasRector, Erik Renström]
  • A. Erik Bäckström
    Erik Bäckström is a notable individual who carries the Swedish surname Bäckström.
  • B. Erik Carlsson
    Erik Carlsson was a legendary Swedish rally driver famed for his success in international competitions during the 1950s and 1960s, particularly with Saab.
  • C. Andreas Norlén
    Andreas Norlén is a Swedish politician who serves as the Speaker of the Riksdag, Sweden’s national legislature.
  • D. Lars Gustafsson
    Lars Gustafsson was a prominent Swedish novelist, poet, and philosopher known for his intellectually playful, metafictional works and significant influence on late 20th-century Scandinavian literature.
  • E. Mikael Odenberg
    Mikael Odenberg is a Swedish politician who served as Sweden’s Minister for Defence and has been a prominent member of the Moderate Party.
  • 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: Erik Renström
Triple: [Lund University, hasRector, Erik Renström]
Generated description
Erik Renström is a Swedish academic and professor who serves as the rector (vice-chancellor) of Lund University.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Erik Renström
Target entity description: Erik Renström is a Swedish academic and professor who serves as the rector (vice-chancellor) of Lund University.
  • A. Erik Bäckström
    Erik Bäckström is a notable individual who carries the Swedish surname Bäckström.
  • B. Erik Carlsson
    Erik Carlsson was a legendary Swedish rally driver famed for his success in international competitions during the 1950s and 1960s, particularly with Saab.
  • C. Andreas Norlén
    Andreas Norlén is a Swedish politician who serves as the Speaker of the Riksdag, Sweden’s national legislature.
  • D. Lars Gustafsson
    Lars Gustafsson was a prominent Swedish novelist, poet, and philosopher known for his intellectually playful, metafictional works and significant influence on late 20th-century Scandinavian literature.
  • E. Mikael Odenberg
    Mikael Odenberg is a Swedish politician who served as Sweden’s Minister for Defence and has been a prominent member of the Moderate Party.
  • 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_69a88aa72d348190a9544bb5b8a4e71d completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abbf0e92248190a9449fce4044438a completed March 7, 2026, 6 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5da963308190bcd0918b8317b3f6 completed March 9, 2026, 5:42 a.m.
NEDg Description generation batch_69ae5e30a69c8190a3f77e784401f671 completed March 9, 2026, 5:44 a.m.
NED2 Entity disambiguation (via description) batch_69ae5ea7909c8190a93d87a5d07b84d4 completed March 9, 2026, 5:46 a.m.
Created at: March 4, 2026, 7:45 p.m.