Triple
T19203645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yangqing Jia |
E480174
|
entity |
| Predicate | hasAcademicAdvisor |
P167
|
FINISHED |
| Object | Trevor Darrell |
—
|
NE NERFINISHED |
How this triple was built (2 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: [Yangqing Jia, hasAcademicAdvisor, Trevor Darrell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trevor Darrell Context triple: [Yangqing Jia, hasAcademicAdvisor, Trevor Darrell]
-
A.
Trevor Darrell
chosen
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.
-
B.
Trevor Albert
Trevor Albert is a film producer best known for his work on the classic comedy "Groundhog Day."
-
C.
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.
-
D.
Trevor Duncan
Trevor Duncan was a British composer best known for his prolific production music and film scores in the mid-20th century.
-
E.
Trevor Jim
Trevor Jim was a computer scientist and cryptographer known for his work on programming languages, security, and formal methods.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5f99a571c8190a1d53eb1994e0058 |
completed | April 20, 2026, 10:02 a.m. |
Created at: April 10, 2026, 1:15 p.m.