Triple

T16598677
Position Surface form Disambiguated ID Type / Status
Subject Iliou Persis E403274 entity
Predicate featuresCharacter P626 FINISHED
Object Helen
Helen is the legendary queen of Sparta whose abduction by Paris sparked the Trojan War in Greek mythology.
E145584 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: [Iliou Persis, featuresCharacter, Helen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen
Context triple: [Iliou Persis, featuresCharacter, Helen]
  • A. Helen
    Helen is a central character in Ernest Hemingway’s short story “The Snows of Kilimanjaro,” portrayed as the wealthy, devoted wife and companion of the writer Harry during his final, reflective days in Africa.
  • B. Helen
    Helen is the given name of H. T. Lowe-Porter, the American translator best known for bringing Thomas Mann’s works into English.
  • C. Helen
    Helen is the daring, quick-thinking heroine of the early 20th-century silent film serial "The Hazards of Helen," known for her action-packed, stunt-filled adventures.
  • D. Helen
    Helen is the birth name of British television presenter Tess Daly, best known for co-hosting the BBC dance competition show "Strictly Come Dancing."
  • E. Helen
    Helen is a Greek and Danish princess of the early 20th century, known as Princess Helen of Greece and Denmark.
  • 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: [Iliou Persis, featuresCharacter, Helen]
Generated description
Helen is the legendary queen of Sparta whose abduction by Paris sparked the Trojan War in Greek mythology.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Helen
Target entity description: Helen is the legendary queen of Sparta whose abduction by Paris sparked the Trojan War in Greek mythology.
  • A. Helen chosen
    Helen is a figure from Greek mythology famed for her extraordinary beauty, whose abduction by Paris sparked the Trojan War.
  • B. Helen
    Helen is a Greek and Danish princess of the early 20th century, known as Princess Helen of Greece and Denmark.
  • C. Helen
    Helen is a feminine given name of Greek origin, traditionally associated with beauty and light and popular in many English-speaking countries.
  • D. Helen
    Helen is a fictional protagonist associated with a narrative set in or around New York City's Central Park.
  • E. Helen
    Helen is the daring, quick-thinking heroine of the early 20th-century silent film serial "The Hazards of Helen," known for her action-packed, stunt-filled adventures.
  • F. None of above.

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_69d883880d0c81908b5fcd454e767b60 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e35d74738c81909711654cf38af150 completed April 18, 2026, 10:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00759dea5c819083fe9fb7dee37a35 completed May 10, 2026, 12:10 p.m.
NEDg Description generation batch_6a0076468d588190972639d0a22f0e3e completed May 10, 2026, 12:12 p.m.
NED2 Entity disambiguation (via description) batch_6a007728680c819082a3bd7e84edb2b0 completed May 10, 2026, 12:16 p.m.
Created at: April 10, 2026, 5:16 a.m.