Triple
T18724478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Language Models are Few-Shot Learners |
E457860
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Mark Chen |
—
|
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: Mark Chen | Statement: [Language Models are Few-Shot Learners, author, Mark Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Chen Context triple: [Language Models are Few-Shot Learners, author, Mark Chen]
-
A.
Mark Chen
chosen
Mark Chen is an AI researcher known for co-authoring influential work on large language models alongside Tom B. Brown at OpenAI.
-
B.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
C.
Kenneth Tsang
Kenneth Tsang was a prolific Hong Kong actor known for his supporting roles in numerous action and crime films across Hong Kong and Hollywood.
-
D.
Eugene Wong
Eugene Wong is a computer scientist best known for his pioneering contributions to relational database theory and the development of early relational database systems.
-
E.
Ian Chen
Ian Chen is a Taiwanese-American child actor best known for his roles in the TV series "Fresh Off the Boat" and the superhero film "Shazam!".
- 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d72d2c4819080b0d31860976b5e |
completed | April 20, 2026, 12:04 a.m. |
Created at: April 10, 2026, 11:50 a.m.