Triple

T329636
Position Surface form Disambiguated ID Type / Status
Subject Fannie Farmer E6596 entity
Predicate givenName P17 FINISHED
Object Fannie
Fannie is a feminine given name, often used in English-speaking countries and historically associated with figures such as the American cookbook author Fannie Farmer.
 E45356 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: Fannie | Statement: [Fannie Farmer, givenName, Fannie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fannie
Context triple: [Fannie Farmer, givenName, Fannie]
  • A. Lucille
    "Lucille" is a 1977 country song by Kenny Rogers that became one of his signature hits and a classic of the genre.
  • B. Nancy
    Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
  • C. Fay
    Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
  • D. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • E. Maxine
    Maxine is a character featured in the film "Once Again."
  • 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: Fannie
Triple: [Fannie Farmer, givenName, Fannie]
Generated description
Fannie is a feminine given name, often used in English-speaking countries and historically associated with figures such as the American cookbook author Fannie Farmer.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fannie
Target entity description: Fannie is a feminine given name, often used in English-speaking countries and historically associated with figures such as the American cookbook author Fannie Farmer.
  • A. Lucille
    "Lucille" is a 1977 country song by Kenny Rogers that became one of his signature hits and a classic of the genre.
  • B. Nancy
    Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
  • C. Fay
    Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
  • D. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • E. Maxine
    Maxine is a character featured in the film "Once Again."
  • 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_69a2e7933d6c8190bb2592ad13286ef2 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eaaeb64881909c7ab9bca3378e2b completed Feb. 28, 2026, 1:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3e0139e108190b2978216a4bc0a3e completed March 1, 2026, 6:43 a.m.
NEDg Description generation batch_69a3e1120c1481908c55b2390f322c53 completed March 1, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_69a3e1e50c548190b65e9e7ef76e1dcd completed March 1, 2026, 6:51 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.