Triple

T12605024
Position Surface form Disambiguated ID Type / Status
Subject Fail Safe E300954 entity
Predicate castMember P1668 FINISHED
Object Russell Hardie
Russell Hardie was an American character actor known for supporting roles in mid-20th-century film and television dramas.
E995875 NE FINISHED

How this triple was built (4 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: Russell Hardie | Statement: [Fail Safe, castMember, Russell Hardie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Russell Hardie
Context triple: [Fail Safe, castMember, Russell Hardie]
  • A. Russell Boyd
    Russell Boyd is an acclaimed Australian cinematographer best known for his work on films such as "Master and Commander: The Far Side of the World," for which he won an Academy Award.
  • B. George Harding
    George Harding was an architect known for designing the historic Chennai Central railway station in India.
  • C. Charles Harding
    Charles Harding was an architect known for designing the Golden Dome.
  • D. Ted Harding
    Ted Harding is a notable individual recognized for achievements significant enough to distinguish him among others sharing the Harding surname.
  • E. Arthur Dorman
    Arthur Dorman was a British industrialist best known as a co-founder of the major steel and engineering firm Dorman Long and Co Ltd, which played a significant role in bridge building and heavy industry.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Russell Hardie
Triple: [Fail Safe, castMember, Russell Hardie]
Generated description
Russell Hardie was an American character actor known for supporting roles in mid-20th-century film and television dramas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Russell Hardie
Target entity description: Russell Hardie was an American character actor known for supporting roles in mid-20th-century film and television dramas.
  • A. Russell Boyd
    Russell Boyd is an acclaimed Australian cinematographer best known for his work on films such as "Master and Commander: The Far Side of the World," for which he won an Academy Award.
  • B. George Harding
    George Harding was an architect known for designing the historic Chennai Central railway station in India.
  • C. Charles Harding
    Charles Harding was an architect known for designing the Golden Dome.
  • D. Ted Harding
    Ted Harding is a notable individual recognized for achievements significant enough to distinguish him among others sharing the Harding surname.
  • E. Arthur Dorman
    Arthur Dorman was a British industrialist best known as a co-founder of the major steel and engineering firm Dorman Long and Co Ltd, which played a significant role in bridge building and heavy industry.
  • F. None of above. chosen

Provenance (5 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_69d7bdea2ca881908f379526c13b1145 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d954e7f2dc8190a42cab7a0e5ea7f3 completed April 10, 2026, 7:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f66869c0b08190b13bcebbe354cd98 completed May 2, 2026, 9:11 p.m.
NEDg Description generation batch_69f66a602f088190984fa3381b5be944 completed May 2, 2026, 9:19 p.m.
NED2 Entity disambiguation (via description) batch_69f66b18e68881908bf79ab52bcc909e completed May 2, 2026, 9:22 p.m.
Created at: April 9, 2026, 5:10 p.m.