Triple

T3810263
Position Surface form Disambiguated ID Type / Status
Subject Samuel Osgood E93115 entity
Predicate name P16 FINISHED
Object Samuel Osgood E93115 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: Samuel Osgood | Statement: [Samuel Osgood, name, Samuel Osgood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Samuel Osgood
Context triple: [Samuel Osgood, name, Samuel Osgood]
  • A. Samuel Osgood chosen
    Samuel Osgood was an American merchant, Revolutionary War officer, and statesman who became the first Postmaster General of the United States under the Constitution.
  • B. James Warren Sever
    James Warren Sever was a Harvard University alumnus and benefactor after whom the historic Sever Hall in Harvard Yard is named.
  • C. Philip Schuyler Green
    Philip Schuyler Green is the main character in the 1947 film "Gentleman's Agreement," a journalist who poses as Jewish to investigate antisemitism.
  • D. Alvan Adams
    Alvan Adams is a former American professional basketball player best known as a versatile center/forward for the Phoenix Suns during the 1970s and 1980s.
  • E. John Buckman
    John Buckman is an American entrepreneur and founder of the online music platform Magnatune and the book-sharing website BookMooch, known for his advocacy of open content and alternative licensing models.
  • 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_69aed96a60088190ab1df8390fffc935 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aee80e178081908cff71223bbf6c43 completed March 9, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb33db9c81908b462ee80aaaad34 completed March 14, 2026, 6:07 a.m.
Created at: March 9, 2026, 3:16 p.m.