Triple

T16071567
Position Surface form Disambiguated ID Type / Status
Subject Princess Helen of Greece and Denmark E389874 entity
Predicate givenName P17 FINISHED
Object Helen
Helen is a Greek and Danish princess of the early 20th century, known as Princess Helen of Greece and Denmark.
E1192980 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: [Princess Helen of Greece and Denmark, givenName, Helen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen
Context triple: [Princess Helen of Greece and Denmark, givenName, 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: [Princess Helen of Greece and Denmark, givenName, Helen]
Generated description
Helen is a Greek and Danish princess of the early 20th century, known as Princess Helen of Greece and Denmark.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Helen
Target entity description: Helen is a Greek and Danish princess of the early 20th century, known as Princess Helen of Greece and Denmark.
  • A. Helen
    Helen is the given first name of Princess Alexandra, The Honourable Lady Ogilvy, a member of the British royal family.
  • B. Helen
    Helen is the given name of Lady Helen Taylor, a British aristocrat and member of the extended royal family known for her work in the arts and fashion.
  • 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 figure from Greek mythology famed for her extraordinary beauty, whose abduction by Paris sparked the Trojan War.
  • 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. 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_69d86daf32ec8190a8c0466c8f49c3c0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e183be909c8190ac6c37ab047151ae completed April 17, 2026, 12:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe4827cd48190aa470c6537e72508 completed May 10, 2026, 1:50 a.m.
NEDg Description generation batch_69ffe6c956a48190845faac983b9a064 completed May 10, 2026, 2 a.m.
NED2 Entity disambiguation (via description) batch_69ffe7387b38819094e55ae14ec2c036 completed May 10, 2026, 2:02 a.m.
Created at: April 10, 2026, 4:57 a.m.