Triple

T3992997
Position Surface form Disambiguated ID Type / Status
Subject Kiko E87034 entity
Predicate creator P184 FINISHED
Object Ruth Rose E55210 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: Ruth Rose | Statement: [Kiko, creator, Ruth Rose]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ruth Rose
Context triple: [Kiko, creator, Ruth Rose]
  • A. Ruth Rose chosen
    Ruth Rose was an American screenwriter best known for co-writing the classic 1933 monster film "King Kong."
  • B. Ruth Hopkins
    Ruth Hopkins was a member of the prominent Hopkins family of early colonial New England, known primarily through her relationship to Mayflower passenger and Plymouth Colony leader Stephen Hopkins.
  • C. Ruth Rumsey
    Ruth Rumsey was the wife of William J. Donovan, the famed American soldier, lawyer, and head of the Office of Strategic Services during World War II.
  • D. Ruth Snyder
    Ruth Snyder was an American woman infamously executed in 1928 for the murder of her husband, a case that became notorious due to a secretly photographed image of her electrocution published in the press.
  • E. Ruth Elizabeth Davis
    Ruth Elizabeth Davis, better known as Bette Davis, was a legendary American film actress renowned for her intense performances and pioneering portrayals of complex, independent women in Hollywood’s Golden Age.
  • 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_69aed94118148190975e6aa4e554cde9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa1c476c819094063f654aa015c4 completed March 9, 2026, 4:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b4447e08190aa90dd50f304a491 completed March 14, 2026, 2:05 p.m.
Created at: March 9, 2026, 3:33 p.m.