Triple

T20078196
Position Surface form Disambiguated ID Type / Status
Subject Paris E499926 entity
Predicate belovedOf P9994 FINISHED
Object Helen
Helen is the mythological queen of Sparta whose abduction by Paris sparked the Trojan War in Greek legend.
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: [Paris, belovedOf, Helen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen
Context triple: [Paris, belovedOf, Helen]
  • A. Helen
    Helen is the birth name of P. L. Travers, the Australian-British author best known for creating the "Mary Poppins" series.
  • B. 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.
  • C. 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.
  • D. Helen
    Helen is the given name of H. T. Lowe-Porter, the American translator best known for bringing Thomas Mann’s works into English.
  • E. Helen
    Helen is a fictional character from the 1930 aviation war film "Hell's Angels," which is renowned for its groundbreaking aerial combat sequences and early sound-era spectacle.
  • 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: [Paris, belovedOf, Helen]
Generated description
Helen is the mythological queen of Sparta whose abduction by Paris sparked the Trojan War in Greek legend.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Helen
Target entity description: Helen is the mythological queen of Sparta whose abduction by Paris sparked the Trojan War in Greek legend.
  • 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 tragedy by Euripides that reimagines the myth of Helen of Troy by portraying her as an innocent woman whose phantom was taken to Troy while she remained in Egypt.
  • C. Helen
    Helen is a Greek and Danish princess of the early 20th century, known as Princess Helen of Greece and Denmark.
  • D. Helen
    Helen is a feminine given name of Greek origin, traditionally associated with beauty and light and popular in many English-speaking countries.
  • E. Helen
    Helen is a fictional protagonist associated with a narrative set in or around New York City's Central Park.
  • 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_69da627770948190997f486f9a2e370f completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6643e216c819088c002fc1de2772a completed April 20, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a081f43b37c819098e55bab84433896 completed May 16, 2026, 7:39 a.m.
NEDg Description generation batch_6a0820057ee8819091d299de1559e1e2 completed May 16, 2026, 7:43 a.m.
NED2 Entity disambiguation (via description) batch_6a08208c203c819083abea34d10d5e4e completed May 16, 2026, 7:45 a.m.
Created at: April 11, 2026, 3:40 p.m.