Triple

T22965522
Position Surface form Disambiguated ID Type / Status
Subject Penny Chenery E571032 entity
Predicate givenName P17 FINISHED
Object Helen
Helen is the given first name of Penny Chenery, the American racehorse owner best known for owning and managing the legendary Thoroughbred Secretariat.
E1563086 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: [Penny Chenery, givenName, Helen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen
Context triple: [Penny Chenery, givenName, Helen]
  • A. Helen
    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 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 fictional protagonist associated with a narrative set in or around New York City's Central Park.
  • D. 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.
  • 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: [Penny Chenery, givenName, Helen]
Generated description
Helen is the given first name of Penny Chenery, the American racehorse owner best known for owning and managing the legendary Thoroughbred Secretariat.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Helen
Target entity description: Helen is the given first name of Penny Chenery, the American racehorse owner best known for owning and managing the legendary Thoroughbred Secretariat.
  • 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 the given first name of Violet Bonham Carter, a prominent British Liberal politician and orator of the 20th century.
  • D. Helen
    Helen is the given first name of the British philosopher and life peer Mary Warnock.
  • 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

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_69e245b2c6548190a0e4c7f2f7df2d48 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1822e542c8190a865f18e64fc0768 completed April 29, 2026, 3:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bca0e6c648190a8bf2d62c32e8313 completed May 19, 2026, 2:25 a.m.
NEDg Description generation batch_6a0bcaad8ba081908008b333976e5cfe completed May 19, 2026, 2:27 a.m.
NED2 Entity disambiguation (via description) batch_6a0bcb4df0308190824c0ad812daf361 completed May 19, 2026, 2:30 a.m.
Created at: April 17, 2026, 3:47 p.m.