Triple
T4654805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Lattner |
E102382
|
entity |
| Predicate | doctoralAdvisor |
P167
|
FINISHED |
| Object | Vikram Adve |
E291879
|
NE FINISHED |
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: Vikram Adve | Statement: [Chris Lattner, doctoralAdvisor, Vikram Adve]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vikram Adve Context triple: [Chris Lattner, doctoralAdvisor, Vikram Adve]
-
A.
Vikram Adve
chosen
Vikram Adve is a computer scientist best known as a co-creator of the LLVM compiler infrastructure and a professor at the University of Illinois at Urbana-Champaign.
-
B.
Sanjay Jain
Sanjay Jain is an economist recognized for his academic contributions and scholarship associated with the Delhi School of Economics.
-
C.
Vijay Joshi
Vijay Joshi is an Indian economist known for his influential work on macroeconomic policy and development, particularly in the context of the Indian economy.
-
D.
Raj Jain
Raj Jain is a prominent computer scientist known for his influential contributions to computer networking and performance analysis.
-
E.
Neil K. Garg
Neil K. Garg is an American organic chemist and UCLA professor renowned for his innovative work in synthetic methodology and chemistry education.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6317ba70819089145766d3462e57 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be1040dbdc8190b9ab7b0b58bca308 |
completed | March 21, 2026, 3:28 a.m. |
Created at: March 20, 2026, 1:14 p.m.