Triple

T16095780
Position Surface form Disambiguated ID Type / Status
Subject Hard Eight E390478 entity
Predicate followsCharacter P10688 FINISHED
Object Sydney
Sydney is the seasoned professional gambler and mentor who serves as the central character in Paul Thomas Anderson’s crime drama film "Hard Eight."
 E1194217 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: Sydney | Statement: [Hard Eight, followsCharacter, Sydney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sydney
Context triple: [Hard Eight, followsCharacter, Sydney]
  • A. Sydney
    Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
  • B. Sydney
    Sydney is the spirited, fashionable young woman who serves as the central heroine of Louisa May Alcott’s novel "An Old-Fashioned Girl."
  • C. Sydney
    Sydney is a unisex given name of Old English origin meaning "wide island" that is used in various English-speaking countries.
  • D. Sydney
    Sydney is a character in the British period drama series "Lark Rise to Candleford," which portrays life in two contrasting rural communities in late 19th-century England.
  • E. Sydney
    Sydney is a recurring character in Alison Bechdel’s long-running comic strip "Dykes to Watch Out For," known for her sharp intellect and complex personal relationships within its ensemble cast.
  • 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: Sydney
Triple: [Hard Eight, followsCharacter, Sydney]
Generated description
Sydney is the seasoned professional gambler and mentor who serves as the central character in Paul Thomas Anderson’s crime drama film "Hard Eight."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sydney
Target entity description: Sydney is the seasoned professional gambler and mentor who serves as the central character in Paul Thomas Anderson’s crime drama film "Hard Eight."
  • A. Sydney
    Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
  • B. Sydney
    Sydney is the spirited, fashionable young woman who serves as the central heroine of Louisa May Alcott’s novel "An Old-Fashioned Girl."
  • C. Sydney
    Sydney is a recurring character in Alison Bechdel’s long-running comic strip "Dykes to Watch Out For," known for her sharp intellect and complex personal relationships within its ensemble cast.
  • D. Sydney
    Sydney is a unisex given name of Old English origin meaning "wide island" that is used in various English-speaking countries.
  • E. Sydney
    Sydney is a character in the British period drama series "Lark Rise to Candleford," which portrays life in two contrasting rural communities in late 19th-century England.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e1859283008190baac96142b5c7e53 completed April 17, 2026, 12:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeb917b008190b1680b347cfa0892 completed May 10, 2026, 2:21 a.m.
NEDg Description generation batch_69ffecb8c71481908b4913bb078b6415 completed May 10, 2026, 2:26 a.m.
NED2 Entity disambiguation (via description) batch_69ffed469a5c8190932fa4ebc44358c4 completed May 10, 2026, 2:28 a.m.
Created at: April 10, 2026, 4:59 a.m.