Triple

T2934594
Position Surface form Disambiguated ID Type / Status
Subject Anya Taylor-Joy E79235 entity
Predicate givenName P17 FINISHED
Object Anya
Anya is the given name of actress Anya Taylor-Joy, known for her roles in films like "The Witch" and the series "The Queen's Gambit."
 E311675 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: Anya | Statement: [Anya Taylor-Joy, givenName, Anya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anya
Context triple: [Anya Taylor-Joy, givenName, Anya]
  • A. Anya
    Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
  • B. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • C. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • D. Sonya
    Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
  • E. Aloysya
    Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
  • 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: Anya
Triple: [Anya Taylor-Joy, givenName, Anya]
Generated description
Anya is the given name of actress Anya Taylor-Joy, known for her roles in films like "The Witch" and the series "The Queen's Gambit."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anya
Target entity description: Anya is the given name of actress Anya Taylor-Joy, known for her roles in films like "The Witch" and the series "The Queen's Gambit."
  • A. Anya
    Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
  • B. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • C. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • D. Sonya
    Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
  • E. Aloysya
    Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
  • 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_69ad8b0fbab081908f6a61567c045d8d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad983c84688190aa7ed5b8091fb140 completed March 8, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0867ba1b48190a54d00c32b075548 completed March 10, 2026, 9 p.m.
NEDg Description generation batch_69b0dbda7ab881908b5a3e1e897fcb49 completed March 11, 2026, 3:04 a.m.
NED2 Entity disambiguation (via description) batch_69b0dc3ed9e08190ac6c6357f0084ba6 completed March 11, 2026, 3:06 a.m.
Created at: March 8, 2026, 2:56 p.m.