Triple

T16859443
Position Surface form Disambiguated ID Type / Status
Subject Hyundai Tucson E409868 entity
Predicate hasTrimLevel P2393 FINISHED
Object SEL
SEL is a mid-level trim of the Hyundai Tucson compact SUV that adds enhanced comfort, convenience, and technology features over the base model.
 E1236827 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: SEL | Statement: [Hyundai Tucson, hasTrimLevel, SEL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SEL
Context triple: [Hyundai Tucson, hasTrimLevel, SEL]
  • A. SEL
    SEL is the former IATA airport code that once designated Seoul’s main international airport before it was replaced by newer facilities.
  • B. SEN
    SEN is the National Rail station code for Shenstone railway station in Staffordshire, England.
  • C. SEN
    SEN is the official FIFA trigramme used to represent the Senegal national football team in international competitions and records.
  • D. SEN
    SEN is the three-letter IATA airport code for London Southend Airport in the United Kingdom.
  • E. Sel
    Sel is a municipality in Innlandet county, Norway, known for its mountainous landscapes and location in the Gudbrandsdalen valley.
  • 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: SEL
Triple: [Hyundai Tucson, hasTrimLevel, SEL]
Generated description
SEL is a mid-level trim of the Hyundai Tucson compact SUV that adds enhanced comfort, convenience, and technology features over the base model.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SEL
Target entity description: SEL is a mid-level trim of the Hyundai Tucson compact SUV that adds enhanced comfort, convenience, and technology features over the base model.
  • A. SEL
    SEL is the former IATA airport code that once designated Seoul’s main international airport before it was replaced by newer facilities.
  • B. SEN
    SEN is the three-letter IATA airport code for London Southend Airport in the United Kingdom.
  • C. SEN
    SEN is the National Rail station code for Shenstone railway station in Staffordshire, England.
  • D. SEN
    SEN is the official FIFA trigramme used to represent the Senegal national football team in international competitions and records.
  • E. Sel
    Sel is a municipality in Innlandet county, Norway, known for its mountainous landscapes and location in the Gudbrandsdalen valley.
  • 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_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b501f72881909f7600311705fb33 completed April 18, 2026, 4:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb25300c8190a352037c21c244bd completed May 10, 2026, 5:06 p.m.
NEDg Description generation batch_6a00bbc80d54819092de4ee363508b49 completed May 10, 2026, 5:09 p.m.
NED2 Entity disambiguation (via description) batch_6a00bc633abc8190a86808986ba294ec completed May 10, 2026, 5:12 p.m.
Created at: April 10, 2026, 5:24 a.m.