Triple
T4651201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Language Models are Few-Shot Learners |
E102297
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Aditya Ramesh |
E437211
|
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: Aditya Ramesh | Statement: [Language Models are Few-Shot Learners, author, Aditya Ramesh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aditya Ramesh Context triple: [Language Models are Few-Shot Learners, author, Aditya Ramesh]
-
A.
Aditya Ramesh
chosen
Aditya Ramesh is a computer scientist and AI researcher best known for leading the development of OpenAI’s CLIP and DALL·E models.
-
B.
Arvind Neelakantan
Arvind Neelakantan is a researcher in artificial intelligence and machine learning known for his work on large language models and neural network architectures.
-
C.
Ashvin Desai
Ashvin Desai is known primarily as the husband of acclaimed Indian novelist Anita Desai.
-
D.
Ravi Menon
Ravi Menon is a Singaporean economist and central banker who has served as the long-time managing director of the Monetary Authority of Singapore, playing a key role in shaping the country’s financial and monetary policy.
-
E.
Sanjay Reddy
Sanjay Reddy is an Indian economist known for his work in development economics, poverty measurement, and global justice.
- 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_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd630343f88190954d19fcd18a5864 |
completed | March 20, 2026, 3:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be39b295ec8190bab91913ddf8eb8d |
completed | March 21, 2026, 6:24 a.m. |
Created at: March 20, 2026, 1:14 p.m.