Triple

T22791560
Position Surface form Disambiguated ID Type / Status
Subject Orestes (play) E564124 entity
Predicate mainCharacter P1183 FINISHED
Object Helen
Helen is a central figure from Greek mythology, renowned as the beautiful queen whose abduction sparked the Trojan War and who appears in various classical works, including Euripides’ plays.
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: [Orestes (play), mainCharacter, Helen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen
Context triple: [Orestes (play), mainCharacter, Helen]
  • A. 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.
  • B. Helen
    Helen is the birth name of P. L. Travers, the Australian-British author best known for creating the "Mary Poppins" series.
  • C. Helen
    Helen is a fictional protagonist associated with a narrative set in or around New York City's Central Park.
  • D. 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.
  • E. Helen
    Helen is a person characterized in this context by her adversarial relationship with Deacon.
  • 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: [Orestes (play), mainCharacter, Helen]
Generated description
Helen is a central figure from Greek mythology, renowned as the beautiful queen whose abduction sparked the Trojan War and who appears in various classical works, including Euripides’ plays.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Helen
Target entity description: Helen is a central figure from Greek mythology, renowned as the beautiful queen whose abduction sparked the Trojan War and who appears in various classical works, including Euripides’ plays.
  • 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 feminine given name of Greek origin, traditionally associated with beauty and light and popular in many English-speaking countries.
  • D. Helen
    Helen is a Greek and Danish princess of the early 20th century, known as Princess Helen of Greece and Denmark.
  • 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_69e2458185f88190b0045227ee420411 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17c3545fc819084af67cc25e94839 completed April 29, 2026, 3:34 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ba7a098488190ac57cf88793a456c completed May 18, 2026, 11:58 p.m.
NEDg Description generation batch_6a0ba8bb77bc81909c5422f9d73c1e70 completed May 19, 2026, 12:03 a.m.
NED2 Entity disambiguation (via description) batch_6a0ba94752c48190a8892cac5ef862b2 completed May 19, 2026, 12:05 a.m.
Created at: April 17, 2026, 3:30 p.m.