Triple

T4884667
Position Surface form Disambiguated ID Type / Status
Subject Waterworld E109409 entity
Predicate character P662 FINISHED
Object Helen
Helen is a central survivor and maternal figure in the post-apocalyptic film "Waterworld," known for her determination to protect the child Enola and seek the mythical Dryland.
 E477648 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: Helen | Statement: [Waterworld, character, Helen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Helen
Context triple: [Waterworld, character, Helen]
  • A. Helen
    Helen is the birth name of Beatrix Potter, the renowned English writer and illustrator best known for her children's books featuring animal characters such as Peter Rabbit.
  • B. Helen
    Helen is the given first name of Violet Bonham Carter, a prominent British Liberal politician and orator of the 20th century.
  • C. Helen
    Helen is a figure from Greek mythology famed for her extraordinary beauty, whose abduction by Paris sparked the Trojan War.
  • D. Helen
    Helen is the mute, terrorized heroine of the classic 1946 psychological thriller film "The Spiral Staircase."
  • E. Helen
    Helen is a character in Aldous Huxley’s novel "Eyeless in Gaza," representing one of the key figures in the book’s exploration of memory, morality, and personal transformation.
  • 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: Helen
Triple: [Waterworld, character, Helen]
Generated description
Helen is a central survivor and maternal figure in the post-apocalyptic film "Waterworld," known for her determination to protect the child Enola and seek the mythical Dryland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Helen
Target entity description: Helen is a central survivor and maternal figure in the post-apocalyptic film "Waterworld," known for her determination to protect the child Enola and seek the mythical Dryland.
  • A. Helen
    Helen is the birth name of Beatrix Potter, the renowned English writer and illustrator best known for her children's books featuring animal characters such as Peter Rabbit.
  • B. Helen
    Helen is a figure from Greek mythology famed for her extraordinary beauty, whose abduction by Paris sparked the Trojan War.
  • C. Helen
    Helen is the mute, terrorized heroine of the classic 1946 psychological thriller film "The Spiral Staircase."
  • D. Helen
    Helen is the given first name of Violet Bonham Carter, a prominent British Liberal politician and orator of the 20th century.
  • E. Helen
    Helen is a character in Aldous Huxley’s novel "Eyeless in Gaza," representing one of the key figures in the book’s exploration of memory, morality, and personal transformation.
  • 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_69bd440f71348190b99938e59fb7f9a1 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6de3718881908521968fa6e6b444 completed March 20, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69be680bf12c8190a5da2c7f0088cec2 completed March 21, 2026, 9:42 a.m.
NEDg Description generation batch_69be6c25d3448190b2589959a2f221c8 completed March 21, 2026, 10 a.m.
NED2 Entity disambiguation (via description) batch_69be6cf6662c8190bc1b94766c5da1e9 completed March 21, 2026, 10:03 a.m.
Created at: March 20, 2026, 1:27 p.m.