Triple

T3593308
Position Surface form Disambiguated ID Type / Status
Subject Kristiansund E76076 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object KSU
KSU is the vehicle registration code used for motor vehicles registered in Kristiansund, Norway.
 E373444 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: KSU | Statement: [Kristiansund, vehicleRegistrationCode, KSU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KSU
Context triple: [Kristiansund, vehicleRegistrationCode, KSU]
  • A. KSU
    KSU is the vehicle registration code used on license plates for the town of Sucha Beskidzka in Poland.
  • B. Kennesaw State University
    Kennesaw State University is a large public research university in Georgia known for its diverse academic programs and rapidly growing student population.
  • C. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • D. KU
    KU is the commonly used abbreviation for Korea University, one of South Korea’s leading private research universities.
  • E. KU
    KU is the commonly used abbreviation for Kettering University, a private university in Flint, Michigan known for its strong engineering and cooperative education programs.
  • 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: KSU
Triple: [Kristiansund, vehicleRegistrationCode, KSU]
Generated description
KSU is the vehicle registration code used for motor vehicles registered in Kristiansund, Norway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KSU
Target entity description: KSU is the vehicle registration code used for motor vehicles registered in Kristiansund, Norway.
  • A. KSU
    KSU is the vehicle registration code used on license plates for the town of Sucha Beskidzka in Poland.
  • B. Kennesaw State University
    Kennesaw State University is a large public research university in Georgia known for its diverse academic programs and rapidly growing student population.
  • C. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • D. KU
    KU is the commonly used abbreviation for Korea University, one of South Korea’s leading private research universities.
  • E. KU
    KU is the commonly used abbreviation for Kettering University, a private university in Flint, Michigan known for its strong engineering and cooperative education programs.
  • 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_69ad85d8042081908af94a04c410dec0 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc15bbbcc81908d6cf95f8e70c6ca completed March 8, 2026, 6:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69b43307cb548190824bb6704e339bff completed March 13, 2026, 3:53 p.m.
NEDg Description generation batch_69b436ac70a481909a623841b836e1a7 completed March 13, 2026, 4:09 p.m.
NED2 Entity disambiguation (via description) batch_69b43731d4708190abd41b28889c4c88 completed March 13, 2026, 4:11 p.m.
Created at: March 8, 2026, 3:22 p.m.