Triple

T1215801
Position Surface form Disambiguated ID Type / Status
Subject Phil Chenier E26103 entity
Predicate name P16 FINISHED
Object Phil Chenier E26103 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: Phil Chenier | Statement: [Phil Chenier, name, Phil Chenier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Phil Chenier
Context triple: [Phil Chenier, name, Phil Chenier]
  • A. Phil Chenier chosen
    Phil Chenier is a former NBA shooting guard best known for his All-Star career with the Washington Bullets and later work as a basketball broadcaster.
  • B. Peter Gatien
    Peter Gatien is a Canadian-born nightclub impresario best known for owning and operating several iconic New York City clubs in the 1980s and 1990s, including Limelight and Tunnel.
  • C. Van Robichaux
    Van Robichaux is an American screenwriter best known for co-writing the comedy film "Fist Fight" and working on various television and film projects.
  • D. Bob Gaillard
    Bob Gaillard was a prominent American college basketball coach best known for leading the University of San Francisco Dons during the 1970s.
  • E. Kees Cook
    Kees Cook is a prominent open-source and Linux kernel security developer known for his extensive work on hardening the Linux kernel and improving software security practices.
  • 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_69a4948331fc8190b531ac9bec71c491 completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4be059c5c8190a200f09442c22334 completed March 1, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac831fb6bc8190907f36e52489ec5c completed March 7, 2026, 7:57 p.m.
Created at: March 1, 2026, 7:46 p.m.