Triple

T2074633
Position Surface form Disambiguated ID Type / Status
Subject Katalin Karikó E44893 entity
Predicate hasChild P369 FINISHED
Object Susan Francia
Susan Francia is a Hungarian-American rower and two-time Olympic gold medalist for the United States.
 E232833 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: Susan Francia | Statement: [Katalin Karikó, hasChild, Susan Francia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Susan Francia
Context triple: [Katalin Karikó, hasChild, Susan Francia]
  • A. Anna Nolin
    Anna Nolin is an American educator and school district leader who serves as superintendent of the Newton Public Schools in Massachusetts.
  • B. Ann Sadler
    Ann Sadler was the wife of John Harvard, the English clergyman and benefactor after whom Harvard University is named.
  • C. Francine Smith
    Francine Smith is a central character on the animated television series "American Dad!", known as Stan Smith’s quirky, often unpredictable wife with a darkly comedic past.
  • D. Adrienne Fazan
    Adrienne Fazan was an American film editor best known for her long collaboration with MGM and director Vincente Minnelli, including work on classic Hollywood musicals.
  • E. Carol Stevens
    Carol Stevens is best known as one of the former wives of American novelist and journalist Norman Mailer.
  • 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: Susan Francia
Triple: [Katalin Karikó, hasChild, Susan Francia]
Generated description
Susan Francia is a Hungarian-American rower and two-time Olympic gold medalist for the United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Susan Francia
Target entity description: Susan Francia is a Hungarian-American rower and two-time Olympic gold medalist for the United States.
  • A. Anna Nolin
    Anna Nolin is an American educator and school district leader who serves as superintendent of the Newton Public Schools in Massachusetts.
  • B. Ann Sadler
    Ann Sadler was the wife of John Harvard, the English clergyman and benefactor after whom Harvard University is named.
  • C. Francine Smith
    Francine Smith is a central character on the animated television series "American Dad!", known as Stan Smith’s quirky, often unpredictable wife with a darkly comedic past.
  • D. Adrienne Fazan
    Adrienne Fazan was an American film editor best known for her long collaboration with MGM and director Vincente Minnelli, including work on classic Hollywood musicals.
  • E. Carol Stevens
    Carol Stevens is best known as one of the former wives of American novelist and journalist Norman Mailer.
  • 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_69a88916c2b48190a5ca2e9b12cad3ed completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abba116ea0819086d16c3913159e9e completed March 7, 2026, 5:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2730d2548190b51dc61520981ca0 completed March 9, 2026, 1:49 a.m.
NEDg Description generation batch_69ae27e4a6f88190a6af44f2cc822f31 completed March 9, 2026, 1:52 a.m.
NED2 Entity disambiguation (via description) batch_69ae2876710c81909451744f48337998 completed March 9, 2026, 1:55 a.m.
Created at: March 4, 2026, 7:41 p.m.