Triple

T4651212
Position Surface form Disambiguated ID Type / Status
Subject Language Models are Few-Shot Learners E102297 entity
Predicate author P4 FINISHED
Object Christopher Berner E457872 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: Christopher Berner | Statement: [Language Models are Few-Shot Learners, author, Christopher Berner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christopher Berner
Context triple: [Language Models are Few-Shot Learners, author, Christopher Berner]
  • A. Christopher Berner chosen
    Christopher Berner is a researcher and engineer known for his work at OpenAI on large-scale machine learning and language models.
  • B. Erich Gutenberg
    Erich Gutenberg was a prominent German economist and business administration scholar known for fundamentally shaping modern German management theory and production economics.
  • C. Jonathan Alberts
    Jonathan Alberts is a film editor known for his work on independent and character-driven movies, including the romantic drama "Like Crazy."
  • D. Mack Goudy Jr.
    Mack Goudy Jr. is an American musician best known as the bassist for the influential proto-punk band The Stooges.
  • E. Nancy Geschke
    Nancy Geschke is known as the wife of Adobe co-founder Charles Geschke and as a partner in his long personal and philanthropic life.
  • 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.