Triple
T4651209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Language Models are Few-Shot Learners |
E102297
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Scott Gray |
E457869
|
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: Scott Gray | Statement: [Language Models are Few-Shot Learners, author, Scott Gray]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Gray Context triple: [Language Models are Few-Shot Learners, author, Scott Gray]
-
A.
Scott Gray
chosen
Scott Gray is a machine learning researcher and engineer known for his work on large-scale neural networks and contributions to OpenAI’s language model research.
-
B.
Graeme Revell
Graeme Revell is a New Zealand-born composer best known for his atmospheric film scores across genres including horror, action, and science fiction.
-
C.
Graham Russell
Graham Russell is an English musician and songwriter best known as the co-founder and guitarist of the soft rock duo Air Supply.
-
D.
Graham Walters
Graham Walters is a film producer best known for his work on the acclaimed Pixar animated feature "Finding Nemo."
-
E.
Patrick Snodgrass
Patrick Snodgrass is an individual notable enough to be recognized as a namesake of the surname Snodgrass.
- 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_69be0374967c8190b77bcd3ea1c4d59d |
completed | March 21, 2026, 2:33 a.m. |
Created at: March 20, 2026, 1:14 p.m.