Triple

T9397965
Position Surface form Disambiguated ID Type / Status
Subject Yelena E226393 entity
Predicate equivalentTo P6530 FINISHED
Object Helen
Helen is a female given name of Greek origin, commonly associated with the mythological figure Helen of Troy and used in many languages with variants such as Yelena.
E791531 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: [Yelena, equivalentTo, Helen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen
Context triple: [Yelena, equivalentTo, 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 the given first name of Violet Bonham Carter, a prominent British Liberal politician and orator of the 20th century.
  • C. 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.
  • D. Helen
    Helen is the central character in the novel "The Spare Room," around whom the story’s emotional and narrative developments revolve.
  • E. Helen
    Helen is the given name of H. T. Lowe-Porter, the American translator best known for bringing Thomas Mann’s works into English.
  • 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: [Yelena, equivalentTo, Helen]
Generated description
Helen is a female given name of Greek origin, commonly associated with the mythological figure Helen of Troy and used in many languages with variants such as Yelena.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Helen
Target entity description: Helen is a female given name of Greek origin, commonly associated with the mythological figure Helen of Troy and used in many languages with variants such as Yelena.
  • A. Helen chosen
    Helen is a feminine given name of Greek origin, traditionally associated with beauty and light and popular in many English-speaking countries.
  • 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 given name of Maria Helen Van Schaack, likely used as her primary personal name.
  • 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 given first name of Violet Bonham Carter, a prominent British Liberal politician and orator of the 20th century.
  • 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_69ca843170f88190800a8ab2b5fc568e completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd51541020819097da2eb60be73760 completed April 1, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1011aef64819085cbb7e04c2d87b2 completed April 4, 2026, 12:16 p.m.
NEDg Description generation batch_69d1054d18ec8190b88dc95f785a370a completed April 4, 2026, 12:34 p.m.
NED2 Entity disambiguation (via description) batch_69d105b7570481909f90b7588e210667 completed April 4, 2026, 12:36 p.m.
Created at: March 30, 2026, 7:46 p.m.