Triple

T10608166
Position Surface form Disambiguated ID Type / Status
Subject Henry Simmons E275930 entity
Predicate name P16 FINISHED
Object Henry Simmons E275930 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: Henry Simmons | Statement: [Henry Simmons, name, Henry Simmons]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Henry Simmons
Context triple: [Henry Simmons, name, Henry Simmons]
  • A. Henry Simmons chosen
    Henry Simmons is an American actor best known for his roles on the television series NYPD Blue and Marvel's Agents of S.H.I.E.L.D.
  • B. Johnny Simmons
    Johnny Simmons is an American actor known for his roles in films like "Scott Pilgrim vs. the World," "The Perks of Being a Wallflower," and the Netflix series "Girlboss."
  • C. Jake Simmonds
    Jake Simmonds is a character from the Doctor Who universe who appears in the two-part story involving the rise of the Cybermen in a parallel Earth.
  • D. Michael Simmons
    Michael Simmons is a relatively common personal name shared by multiple individuals across fields such as sports, business, and the arts, rather than referring to one singular widely recognized figure.
  • E. Dane Witherspoon
    Dane Witherspoon was an American actor best known for his roles in daytime soap operas during the 1980s.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6df4c38c881908f69bb757b8e03f5 completed April 8, 2026, 11:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69d95eb726bc8190a8db7357bd126016 completed April 10, 2026, 8:33 p.m.
Created at: April 8, 2026, 7:32 p.m.