Triple

T820472
Position Surface form Disambiguated ID Type / Status
Subject Hartland Snyder E17740 entity
Predicate hasFamilyName P18 FINISHED
Object Snyder E61047 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: Snyder | Statement: [Hartland Snyder, hasFamilyName, Snyder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Snyder
Context triple: [Hartland Snyder, hasFamilyName, Snyder]
  • A. Snyder chosen
    Snyder is a surname most prominently associated with Dan Snyder, the American businessman and former owner of the NFL’s Washington Commanders.
  • B. Nolan
    Nolan is a common Irish surname that has been borne by numerous notable figures across fields such as film, sports, and politics.
  • C. Sullivan
    Sullivan is a shortened name for the international law firm Sullivan & Worcester LLP, known for its corporate, tax, and financial legal services.
  • D. Miller
    Miller is a common English and Scottish occupational surname historically given to people who worked in grain mills.
  • E. Tucker
    Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
  • 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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab6698d881908d8c5d91259f97ec completed March 1, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d91c2948190bc13a223548facba completed March 3, 2026, 11:24 p.m.
Created at: March 1, 2026, 7:38 p.m.