Triple
T4651200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Language Models are Few-Shot Learners |
E102297
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Rewon Child |
E99317
|
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: Rewon Child | Statement: [Language Models are Few-Shot Learners, author, Rewon Child]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rewon Child Context triple: [Language Models are Few-Shot Learners, author, Rewon Child]
-
A.
Rewon Child
chosen
Rewon Child is a machine learning researcher known for co-authoring the influential GPT-2 language model paper at OpenAI.
-
B.
Sojin
Sojin is a given name, often used in East Asian cultures, that can refer to various individuals in entertainment, arts, and other fields.
-
C.
Soohorang
Soohorang is the white tiger character that served as the official mascot of the 2018 Winter Olympics in Pyeongchang, South Korea.
-
D.
In-hwoi
In-hwoi is the given name of Koo In-hwoi, the South Korean entrepreneur who founded the LG Group conglomerate.
-
E.
Jin
Jin is a Chinese surname historically associated with the Jewish community of Kaifeng, reflecting their integration into Chinese society while preserving distinct communal identities.
- 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.