Triple

T9189859
Position Surface form Disambiguated ID Type / Status
Subject Orneta E220553 entity
Predicate carPlatesCode P1173 FINISHED
Object NLI
NLI is the vehicle registration code used on license plates for cars registered in Orneta, Poland.
E783875 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: NLI | Statement: [Orneta, carPlatesCode, NLI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: NLI
Context triple: [Orneta, carPlatesCode, NLI]
  • A. NLC
    NLC is the National Rail station code for New London Union Station in New London, Connecticut.
  • B. NLC
    NLC is an abbreviation commonly used for the National Labor College, a former U.S. institution dedicated to higher education and training for labor union members and leaders.
  • C. NNL
    NNL is the commonly used abbreviation for the Negro National League, a pioneering professional baseball league that was a cornerstone of Negro league baseball in the early 20th century.
  • D. NLS
    NLS is the commonly used abbreviation for the National League System, the tiered structure of men’s football leagues in England below the professional Premier League and English Football League.
  • E. NLS
    NLS (oN-Line System) was an early, pioneering computer system that introduced many foundational concepts of modern computing, including the mouse, hypertext, and collaborative editing.
  • 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: NLI
Triple: [Orneta, carPlatesCode, NLI]
Generated description
NLI is the vehicle registration code used on license plates for cars registered in Orneta, Poland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: NLI
Target entity description: NLI is the vehicle registration code used on license plates for cars registered in Orneta, Poland.
  • A. NLC
    NLC is the National Rail station code for New London Union Station in New London, Connecticut.
  • B. NLC
    NLC is an abbreviation commonly used for the National Labor College, a former U.S. institution dedicated to higher education and training for labor union members and leaders.
  • C. NNL
    NNL is the commonly used abbreviation for the Negro National League, a pioneering professional baseball league that was a cornerstone of Negro league baseball in the early 20th century.
  • D. NLS
    NLS (oN-Line System) was an early, pioneering computer system that introduced many foundational concepts of modern computing, including the mouse, hypertext, and collaborative editing.
  • E. NLS
    NLS is a U.S. Library of Congress program that provides free accessible reading materials, including braille and audio books, to people who are blind, have low vision, or are print disabled.
  • 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_69ca83e6d77c81909862b7afef56b1bf completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccd5bd8c5c81909d0cdbcd7410fcee completed April 1, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05c272f508190aade1769c88cf16d completed April 4, 2026, 12:32 a.m.
NEDg Description generation batch_69d05cda59f88190bcde5a91aec2f9dd completed April 4, 2026, 12:35 a.m.
NED2 Entity disambiguation (via description) batch_69d05ddd28008190b82c42220871e73f completed April 4, 2026, 12:39 a.m.
Created at: March 30, 2026, 7:24 p.m.