Triple

T1165049
Position Surface form Disambiguated ID Type / Status
Subject Karlsruhe E24580 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object KA
KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
 E134280 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: KA | Statement: [Karlsruhe, vehicleRegistrationCode, KA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KA
Context triple: [Karlsruhe, vehicleRegistrationCode, KA]
  • A. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • B. KE
    KE is the two-letter ISO 3166-1 alpha-2 country code assigned to Kenya for international identification and data standards.
  • C. AK
    AK is the Polish abbreviation for the Home Army, the principal resistance movement in German-occupied Poland during World War II.
  • D. KO
    KO is the New York Stock Exchange ticker symbol for The Coca-Cola Company, one of the world’s largest and most recognizable beverage corporations.
  • E. KC
    KC is a common shorthand nickname for Kansas City, Missouri, a major Midwestern U.S. city known for its jazz heritage, barbecue, and sports teams.
  • 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: KA
Triple: [Karlsruhe, vehicleRegistrationCode, KA]
Generated description
KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KA
Target entity description: KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • A. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • B. KE
    KE is the two-letter ISO 3166-1 alpha-2 country code assigned to Kenya for international identification and data standards.
  • C. AK
    AK is the Polish abbreviation for the Home Army, the principal resistance movement in German-occupied Poland during World War II.
  • D. KO
    KO is the New York Stock Exchange ticker symbol for The Coca-Cola Company, one of the world’s largest and most recognizable beverage corporations.
  • E. KC
    KC is a common shorthand nickname for Kansas City, Missouri, a major Midwestern U.S. city known for its jazz heritage, barbecue, and sports teams.
  • 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_69a494060e148190abb42f971242c197 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bccb3b388190938c68dee90b3f19 completed March 1, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac668290008190a5e175c0a2e5feb0 completed March 7, 2026, 5:55 p.m.
NEDg Description generation batch_69ac6aaa84fc81908130543b14938e41 completed March 7, 2026, 6:12 p.m.
NED2 Entity disambiguation (via description) batch_69ac6b24987c819080e00b49df8f6edb completed March 7, 2026, 6:15 p.m.
Created at: March 1, 2026, 7:45 p.m.