Triple
T13061788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jitendra Malik |
E329214
|
entity |
| Predicate | notableStudent |
P4838
|
FINISHED |
| Object |
Trevor Darrell
Trevor Darrell is a prominent computer vision and machine learning researcher and professor known for his work on deep learning, visual recognition, and autonomous systems.
|
E1017402
|
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: Trevor Darrell | Statement: [Jitendra Malik, notableStudent, Trevor Darrell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trevor Darrell Context triple: [Jitendra Malik, notableStudent, Trevor Darrell]
-
A.
Trevor Albert
Trevor Albert is a film producer best known for his work on the classic comedy "Groundhog Day."
-
B.
Trevor Blackwell
Trevor Blackwell is a Canadian engineer, entrepreneur, and roboticist best known as a co-founder of the startup accelerator Y Combinator and for his work in humanoid and self-balancing robots.
-
C.
Trevor Duncan
Trevor Duncan was a British composer best known for his prolific production music and film scores in the mid-20th century.
-
D.
Trevor Jim
Trevor Jim was a computer scientist and cryptographer known for his work on programming languages, security, and formal methods.
-
E.
Trevor Hawkins
Trevor Hawkins is a relatively obscure individual whose specific public achievements or profession are not widely documented.
- 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: Trevor Darrell Triple: [Jitendra Malik, notableStudent, Trevor Darrell]
Generated description
Trevor Darrell is a prominent computer vision and machine learning researcher and professor known for his work on deep learning, visual recognition, and autonomous systems.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trevor Darrell Target entity description: Trevor Darrell is a prominent computer vision and machine learning researcher and professor known for his work on deep learning, visual recognition, and autonomous systems.
-
A.
Trevor Albert
Trevor Albert is a film producer best known for his work on the classic comedy "Groundhog Day."
-
B.
Trevor Blackwell
Trevor Blackwell is a Canadian engineer, entrepreneur, and roboticist best known as a co-founder of the startup accelerator Y Combinator and for his work in humanoid and self-balancing robots.
-
C.
Trevor Duncan
Trevor Duncan was a British composer best known for his prolific production music and film scores in the mid-20th century.
-
D.
Trevor Jim
Trevor Jim was a computer scientist and cryptographer known for his work on programming languages, security, and formal methods.
-
E.
Trevor Hawkins
Trevor Hawkins is a relatively obscure individual whose specific public achievements or profession are not widely documented.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980e7ee548190b4b18bdb1357c359 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbe45c8c819080fbdf1d94376feb |
completed | May 3, 2026, 4:15 a.m. |
| NEDg | Description generation | batch_69f6cd3d5090819091b65f544ad139fd |
completed | May 3, 2026, 4:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6cdc8d52c819083717a455d589646 |
completed | May 3, 2026, 4:23 a.m. |
Created at: April 9, 2026, 8:59 p.m.