Triple

T16779181
Position Surface form Disambiguated ID Type / Status
Subject Christof E407811 entity
Predicate portrayedBy P1507 FINISHED
Object Ed Harris E29208 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: Ed Harris | Statement: [Christof, portrayedBy, Ed Harris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ed Harris
Context triple: [Christof, portrayedBy, Ed Harris]
  • A. Ed Harris chosen
    Ed Harris is an American actor and filmmaker known for his intense, authoritative performances in films such as "The Truman Show," "Apollo 13," and "Pollock."
  • B. Michael York
    Michael York is an English actor known for his roles in films such as "Cabaret," "Logan's Run," and the "Austin Powers" series.
  • C. Eric S. Roberts
    Eric S. Roberts is a prominent computer scientist and educator known for his influential work in computer science pedagogy, curriculum development, and widely used textbooks.
  • D. Danny Huston
    Danny Huston is an American actor and director known for his character roles in films such as "The Constant Gardener," "X-Men Origins: Wolverine," and "Wonder Woman."
  • E. George Norton
    George Norton was a British colonial-era lawyer and educator best known for establishing Presidency College in Madras, one of India’s earliest and most prestigious institutions of higher learning.
  • 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_69d8839270588190886720d9519bbf8f completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b21401b881909bbbc7382e851a90 completed April 18, 2026, 4:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb0911488190a65c1dc536b6ea3e completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:22 a.m.