Triple

T7652157
Position Surface form Disambiguated ID Type / Status
Subject Daniel Kahneman E173277 entity
Predicate coAuthor P398 FINISHED
Object Cass R. Sunstein E59309 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: Cass R. Sunstein | Statement: [Daniel Kahneman, coAuthor, Cass R. Sunstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cass R. Sunstein
Context triple: [Daniel Kahneman, coAuthor, Cass R. Sunstein]
  • A. Cass Sunstein chosen
    Cass Sunstein is an American legal scholar and Harvard Law professor known for his influential work on constitutional law, behavioral economics, and public policy, including the concept of "nudge."
  • B. Christopher L. Eisgruber
    Christopher L. Eisgruber is an American legal scholar and academic administrator who serves as the president of Princeton University.
  • C. Stephen E. Rivkin
    Stephen E. Rivkin is an American film editor best known for his work on major feature films including the "Avatar" series.
  • D. Geoffrey R. Stone
    Geoffrey R. Stone is an American legal scholar renowned for his work on constitutional law and the First Amendment, and for his long association with the University of Chicago.
  • E. David Rosenblum
    David Rosenblum is a prominent computer scientist known for his influential research in software engineering and formal methods.
  • 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_69c6995473348190a4f41d110d619a18 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c701770ac881909452348c9547ab47 completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c89aeb66c081909f3a3d6385637c25 completed March 29, 2026, 3:22 a.m.
Created at: March 27, 2026, 3:58 p.m.