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.