Triple

T520626
Position Surface form Disambiguated ID Type / Status
Subject Diana Churchill E10806 entity
Predicate givenName P17 FINISHED
Object Diana
Diana is a feminine given name of Latin origin, famously borne by the Roman goddess of the hunt and by Diana, Princess of Wales.
 E71669 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: Diana | Statement: [Diana Churchill, givenName, Diana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Diana
Context triple: [Diana Churchill, givenName, Diana]
  • A. Rachel
    Rachel is a prominent biblical matriarch in the Book of Genesis, known as Jacob’s beloved wife and the mother of Joseph and Benjamin.
  • B. Cynthia
    Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
  • C. Roxana
    Roxana is a feminine given name of Persian origin, historically associated with figures such as the wife of Alexander the Great and later borne by various notable women.
  • D. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • E. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • 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: Diana
Triple: [Diana Churchill, givenName, Diana]
Generated description
Diana is a feminine given name of Latin origin, famously borne by the Roman goddess of the hunt and by Diana, Princess of Wales.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Diana
Target entity description: Diana is a feminine given name of Latin origin, famously borne by the Roman goddess of the hunt and by Diana, Princess of Wales.
  • A. Rachel
    Rachel is a prominent biblical matriarch in the Book of Genesis, known as Jacob’s beloved wife and the mother of Joseph and Benjamin.
  • B. Cynthia
    Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
  • C. Roxana
    Roxana is a feminine given name of Persian origin, historically associated with figures such as the wife of Alexander the Great and later borne by various notable women.
  • D. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • E. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • 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_69a2e84b16c4819088d284c47c3a7968 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1a1817c8190a6cc8f423071d3ad completed Feb. 28, 2026, 1:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4fc7b283c8190af5fb7fa649a9095 completed March 2, 2026, 2:56 a.m.
NEDg Description generation batch_69a4fd82527c81908f996fb4ac1d1000 completed March 2, 2026, 3:01 a.m.
NED2 Entity disambiguation (via description) batch_69a4fdfce6148190998b823f069cd43d completed March 2, 2026, 3:03 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.