Triple

T3338570
Position Surface form Disambiguated ID Type / Status
Subject Sidney Lee E70200 entity
Predicate name P16 FINISHED
Object Sidney Lee E70200 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: Sidney Lee | Statement: [Sidney Lee, name, Sidney Lee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sidney Lee
Context triple: [Sidney Lee, name, Sidney Lee]
  • A. Sidney Lee chosen
    Sidney Lee was a British biographer and literary scholar best known for his extensive work on the Dictionary of National Biography and his influential studies of William Shakespeare.
  • B. Sidney Green
    Sidney Green is a former American professional basketball player best known for his standout college career at UNLV and subsequent NBA tenure in the 1980s.
  • C. Sidney Luft
    Sidney Luft was an American show business figure and film producer best known as the husband and manager of Judy Garland, helping to revive her career in the 1950s.
  • D. Seybourn H. Lynne
    Seybourn H. Lynne was a United States federal judge known for his role in significant civil rights-era cases, including those challenging racial segregation.
  • E. Henry Van Brunt
    Henry Van Brunt was a prominent 19th-century American architect known for his influential role in shaping civic and institutional architecture across the United States.
  • 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_69ad85a405e48190b6e68de7cf9f319e completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1bd6c7c8190b7229de1433d8d20 completed March 8, 2026, 5:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c5e56b081909794cbab0c3e1cc9 completed March 14, 2026, 8:29 a.m.
Created at: March 8, 2026, 3:12 p.m.