Triple
T1216996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray Kurzweil |
E26127
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
Kurzweil OCR (optical character recognition) systems
Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
|
E139159
|
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: Kurzweil OCR (optical character recognition) systems | Statement: [Ray Kurzweil, knownFor, Kurzweil OCR (optical character recognition) systems]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kurzweil OCR (optical character recognition) systems Context triple: [Ray Kurzweil, knownFor, Kurzweil OCR (optical character recognition) systems]
-
A.
OCR
OCR is the Office for Civil Rights, a U.S. government agency responsible for enforcing civil rights laws and ensuring equal access and non-discrimination in federally funded programs.
-
B.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
C.
IAS machine
The IAS machine was an early electronic stored-program computer designed by John von Neumann and his colleagues at the Institute for Advanced Study, serving as a prototype for many subsequent computer architectures.
-
D.
Xerox Star system
The Xerox Star system was an early commercial workstation that pioneered the modern graphical user interface with icons, windows, and a desktop metaphor, profoundly influencing later personal computers.
-
E.
Xerox PARC technical reports
Xerox PARC technical reports are a series of influential research documents produced at Xerox's Palo Alto Research Center that detail pioneering work in computer science, including early graphical user interfaces, networking, and personal computing.
- 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: Kurzweil OCR (optical character recognition) systems Triple: [Ray Kurzweil, knownFor, Kurzweil OCR (optical character recognition) systems]
Generated description
Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kurzweil OCR (optical character recognition) systems Target entity description: Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
-
A.
OCR
OCR is the Office for Civil Rights, a U.S. government agency responsible for enforcing civil rights laws and ensuring equal access and non-discrimination in federally funded programs.
-
B.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
C.
IAS machine
The IAS machine was an early electronic stored-program computer designed by John von Neumann and his colleagues at the Institute for Advanced Study, serving as a prototype for many subsequent computer architectures.
-
D.
Xerox Star system
The Xerox Star system was an early commercial workstation that pioneered the modern graphical user interface with icons, windows, and a desktop metaphor, profoundly influencing later personal computers.
-
E.
Xerox PARC technical reports
Xerox PARC technical reports are a series of influential research documents produced at Xerox's Palo Alto Research Center that detail pioneering work in computer science, including early graphical user interfaces, networking, and personal computing.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be06d6308190a44c505e6b5e8d42 |
completed | March 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac831fb6bc8190907f36e52489ec5c |
completed | March 7, 2026, 7:57 p.m. |
| NEDg | Description generation | batch_69ac839076708190882fe59c80bd3e7e |
completed | March 7, 2026, 7:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac84092f088190894b3ca2a268e5c1 |
completed | March 7, 2026, 8:01 p.m. |
Created at: March 1, 2026, 7:46 p.m.