Triple

T1265828
Position Surface form Disambiguated ID Type / Status
Subject Sarah Churchill E12597 entity
Predicate notableWork P4 FINISHED
Object Danielle
"Danielle" is a work created by Sarah Churchill, known as part of her contributions to the arts.
 E184707 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: Danielle | Statement: [Sarah Churchill, notableWork, Danielle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Danielle
Context triple: [Sarah Churchill, notableWork, Danielle]
  • A. Nicole
    Nicole is a central character in Margaret Atwood's dystopian novel "The Testaments," whose story helps expose and challenge the oppressive regime of Gilead.
  • B. Megan
    Megan is the full first name of Meg Griffin, the often-mocked teenage daughter character from the animated television series "Family Guy."
  • C. Rebeca
    Rebeca is a feminine given name, commonly used in Spanish- and Portuguese-speaking countries, that is a variant of the name Rebecca.
  • D. Diane
    Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
  • E. Vanessa
    Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
  • 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: Danielle
Triple: [Sarah Churchill, notableWork, Danielle]
Generated description
"Danielle" is a work created by Sarah Churchill, known as part of her contributions to the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Danielle
Target entity description: "Danielle" is a work created by Sarah Churchill, known as part of her contributions to the arts.
  • A. Nicole
    Nicole is a central character in Margaret Atwood's dystopian novel "The Testaments," whose story helps expose and challenge the oppressive regime of Gilead.
  • B. Megan
    Megan is the full first name of Meg Griffin, the often-mocked teenage daughter character from the animated television series "Family Guy."
  • C. Rebeca
    Rebeca is a feminine given name, commonly used in Spanish- and Portuguese-speaking countries, that is a variant of the name Rebecca.
  • D. Diane
    Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
  • E. Vanessa
    Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
  • 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_69a4933352e08190ac617291985e76c0 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4c036deb881909b234894347c75c6 completed March 1, 2026, 10:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad58a6e0088190b35258a6accb0440 completed March 8, 2026, 11:08 a.m.
NEDg Description generation batch_69ad5a2d88648190aa22744e4d9ac9d2 completed March 8, 2026, 11:14 a.m.
NED2 Entity disambiguation (via description) batch_69ad5ad5f50c81908541eb94bb1236b6 completed March 8, 2026, 11:17 a.m.
Created at: March 1, 2026, 7:50 p.m.