Triple

T4651188
Position Surface form Disambiguated ID Type / Status
Subject Language Models are Few-Shot Learners E102297 entity
Predicate author P4 FINISHED
Object Nick Ryder E457862 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: Nick Ryder | Statement: [Language Models are Few-Shot Learners, author, Nick Ryder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nick Ryder
Context triple: [Language Models are Few-Shot Learners, author, Nick Ryder]
  • A. Nick Ryder chosen
    Nick Ryder is a researcher and co-author known for collaborating with Tom B. Brown on work in advanced machine learning and artificial intelligence.
  • B. Jon Plowman
    Jon Plowman is a British television producer best known for his influential work on BBC comedies, including series such as Absolutely Fabulous and The Office.
  • C. Rick Yorn
    Rick Yorn is an American talent manager and film producer known for representing major Hollywood actors and producing a range of high-profile movies and television projects.
  • D. Andrew Robinson
    Andrew Robinson is an American actor best known for his roles in films like "Dirty Harry" and the TV series "Star Trek: Deep Space Nine."
  • E. Andrew Rennison
    Andrew Rennison is a British public official known for serving as the inaugural Surveillance Camera Commissioner, overseeing the regulation and ethical use of CCTV and related surveillance technologies in the UK.
  • 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.