Triple

T1252861
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Social Sciences, University of Oslo E26915 entity
Predicate abbreviation P43 FINISHED
Object SV
SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
 E143679 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: SV | Statement: [Faculty of Social Sciences, University of Oslo, abbreviation, SV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SV
Context triple: [Faculty of Social Sciences, University of Oslo, abbreviation, SV]
  • A. SV
    SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
  • B. SD
    SD is the abbreviated name commonly used for the SS Security Service, the intelligence and security agency of Nazi Germany’s Schutzstaffel.
  • C. SD
    SD is the standard abbreviation for the San Diego Padres, a Major League Baseball team based in San Diego, California.
  • D. SD
    SD is the commonly used abbreviation for Norway's Ministry of Transport, the government body responsible for national transport policy and infrastructure.
  • E. SL
    SL is the public transport authority and brand responsible for operating and coordinating the mass transit system in the Stockholm region of Sweden.
  • 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: SV
Triple: [Faculty of Social Sciences, University of Oslo, abbreviation, SV]
Generated description
SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SV
Target entity description: SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
  • A. SV
    SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
  • B. SD
    SD is the abbreviated name commonly used for the SS Security Service, the intelligence and security agency of Nazi Germany’s Schutzstaffel.
  • C. SD
    SD is the standard abbreviation for the San Diego Padres, a Major League Baseball team based in San Diego, California.
  • D. SD
    SD is the commonly used abbreviation for Norway's Ministry of Transport, the government body responsible for national transport policy and infrastructure.
  • E. SL
    SL is the public transport authority and brand responsible for operating and coordinating the mass transit system in the Stockholm region of Sweden.
  • 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_69a49487a9c48190ba9b05348fd1b53f completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4bf875cf48190b6781d41097ee39b completed March 1, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac93c903488190bcbf1928699bafd2 completed March 7, 2026, 9:08 p.m.
NEDg Description generation batch_69ac942d359c81908d2c2acb4c1aa8d0 completed March 7, 2026, 9:10 p.m.
NED2 Entity disambiguation (via description) batch_69ac95cf8800819080d18d559f73dcc2 completed March 7, 2026, 9:17 p.m.
Created at: March 1, 2026, 7:47 p.m.