Triple

T13558949
Position Surface form Disambiguated ID Type / Status
Subject Arliss E323853 entity
Predicate developer P73 FINISHED
Object Robert Wuhl E60523 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: Robert Wuhl | Statement: [Arliss, developer, Robert Wuhl]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robert Wuhl
Context triple: [Arliss, developer, Robert Wuhl]
  • A. Robert Wuhl chosen
    Robert Wuhl is an American actor, comedian, and writer best known for his roles in films like "Bull Durham" and "Batman" and for creating and starring in the HBO series "Arliss."
  • B. Ron Todd
    Ron Todd was a prominent British trade union leader who served as a key figure in the labor movement during the late 20th century.
  • C. Ron Todd
    Ron Todd is an American politician who served as the Kansas Insurance Commissioner before Kathleen Sebelius.
  • D. Michael O'Laughlen
    Michael O'Laughlen was an American Confederate sympathizer and associate of John Wilkes Booth who was implicated as a co-conspirator in the plot surrounding the assassination of President Abraham Lincoln.
  • E. Clarke Peters
    Clarke Peters is an American actor, writer, and director best known for his roles in acclaimed television series such as The Wire and Treme, as well as numerous film and stage performances.
  • 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_69d8076830b48190910a902bae5888e2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaff4223c8190801d153ae8f94c73 completed April 12, 2026, 2:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f794281fb48190882f164df1def07e completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:47 p.m.