Triple

T6574504
Position Surface form Disambiguated ID Type / Status
Subject Elmshorn E155525 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object PI
PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
E602311 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: PI | Statement: [Elmshorn, vehicleRegistrationCode, PI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PI
Context triple: [Elmshorn, vehicleRegistrationCode, PI]
  • A. PI
    PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
  • B. PIP
    PIP is a UK welfare benefit that helps disabled people or those with long-term health conditions cover the extra costs of daily living and mobility.
  • C. PIK
    PIK is a leading German research institute focused on analyzing the causes and impacts of climate change and developing strategies for sustainable solutions.
  • D. PIK
    PIK is the three-letter IATA airport code for Glasgow Prestwick Airport in South Ayrshire, Scotland.
  • E. PIF
    PIF is Saudi Arabia’s sovereign wealth fund, one of the world’s largest state-owned investment funds, financing major domestic and international projects to diversify the kingdom’s economy.
  • 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: PI
Triple: [Elmshorn, vehicleRegistrationCode, PI]
Generated description
PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: PI
Target entity description: PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
  • A. PI
    PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
  • B. PIP
    PIP is a UK welfare benefit that helps disabled people or those with long-term health conditions cover the extra costs of daily living and mobility.
  • C. PIK
    PIK is a leading German research institute focused on analyzing the causes and impacts of climate change and developing strategies for sustainable solutions.
  • D. PIK
    PIK is the three-letter IATA airport code for Glasgow Prestwick Airport in South Ayrshire, Scotland.
  • E. PIF
    PIF is a regional intergovernmental organization that brings together Pacific island countries and territories to cooperate on political, economic, and security issues.
  • 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_69c688151254819080387f87deab8fa7 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae7134708190a0355519d117ab9c completed March 27, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cba0182c8190a30bc5e6fea4680e completed March 27, 2026, 6:25 p.m.
NEDg Description generation batch_69c6cd071be4819090d6adf0e27c99d2 completed March 27, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69c6ce04855481908bfca416fda8c218 completed March 27, 2026, 6:35 p.m.
Created at: March 27, 2026, 1:53 p.m.