Triple
T2739083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takeo Kanade |
E60704
|
entity |
| Predicate | academicPosition |
P298
|
FINISHED |
| Object |
U. A. and Helen Whitaker University Professor at Carnegie Mellon University
The U. A. and Helen Whitaker University Professor at Carnegie Mellon University is a distinguished endowed professorship in computer science and robotics held by pioneering computer vision researcher Takeo Kanade.
|
E295651
|
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: U. A. and Helen Whitaker University Professor at Carnegie Mellon University | Statement: [Takeo Kanade, academicPosition, U. A. and Helen Whitaker University Professor at Carnegie Mellon University]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: U. A. and Helen Whitaker University Professor at Carnegie Mellon University Context triple: [Takeo Kanade, academicPosition, U. A. and Helen Whitaker University Professor at Carnegie Mellon University]
-
A.
School of Computer Science at Carnegie Mellon University
The School of Computer Science at Carnegie Mellon University is a world-renowned academic and research institution recognized for pioneering contributions across computer science, artificial intelligence, robotics, and related fields.
-
B.
Computer Science Department, Carnegie Mellon University
The Computer Science Department at Carnegie Mellon University is a core academic unit renowned for pioneering research and education in computer science within CMU’s School of Computer Science.
-
C.
Language Technologies Institute, Carnegie Mellon University
The Language Technologies Institute at Carnegie Mellon University is a leading research and education center focused on areas such as natural language processing, machine learning for language, speech recognition, and related AI-driven language technologies.
-
D.
board of trustees of Carnegie Mellon University
The board of trustees of Carnegie Mellon University is the institution’s governing body responsible for overseeing its strategic direction, financial health, and overall governance.
-
E.
CMU College of Engineering
CMU College of Engineering is the engineering school of Carnegie Mellon University, renowned for its cutting-edge research and education in areas such as robotics, computer engineering, and materials science.
- 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: U. A. and Helen Whitaker University Professor at Carnegie Mellon University Triple: [Takeo Kanade, academicPosition, U. A. and Helen Whitaker University Professor at Carnegie Mellon University]
Generated description
The U. A. and Helen Whitaker University Professor at Carnegie Mellon University is a distinguished endowed professorship in computer science and robotics held by pioneering computer vision researcher Takeo Kanade.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: U. A. and Helen Whitaker University Professor at Carnegie Mellon University Target entity description: The U. A. and Helen Whitaker University Professor at Carnegie Mellon University is a distinguished endowed professorship in computer science and robotics held by pioneering computer vision researcher Takeo Kanade.
-
A.
School of Computer Science at Carnegie Mellon University
The School of Computer Science at Carnegie Mellon University is a world-renowned academic and research institution recognized for pioneering contributions across computer science, artificial intelligence, robotics, and related fields.
-
B.
Computer Science Department, Carnegie Mellon University
The Computer Science Department at Carnegie Mellon University is a core academic unit renowned for pioneering research and education in computer science within CMU’s School of Computer Science.
-
C.
Language Technologies Institute, Carnegie Mellon University
The Language Technologies Institute at Carnegie Mellon University is a leading research and education center focused on areas such as natural language processing, machine learning for language, speech recognition, and related AI-driven language technologies.
-
D.
board of trustees of Carnegie Mellon University
The board of trustees of Carnegie Mellon University is the institution’s governing body responsible for overseeing its strategic direction, financial health, and overall governance.
-
E.
CMU College of Engineering
CMU College of Engineering is the engineering school of Carnegie Mellon University, renowned for its cutting-edge research and education in areas such as robotics, computer engineering, and materials science.
- 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_69ab4b77febc819095603eb012cd141b |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb147a588190829b74fe05b3a114 |
completed | March 7, 2026, 8 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbc607d88190bc35ce56ac26dbf9 |
completed | March 10, 2026, 6:35 a.m. |
| NEDg | Description generation | batch_69afbce109f48190be1a31d9300dbee6 |
completed | March 10, 2026, 6:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afbda490ac8190bb12598e26b91677 |
completed | March 10, 2026, 6:43 a.m. |
Created at: March 6, 2026, 9:56 p.m.