Triple

T16252349
Position Surface form Disambiguated ID Type / Status
Subject Sweet Thursday E394540 entity
Predicate mainCharacter P1183 FINISHED
Object Suzy
Suzy is the central female protagonist of John Steinbeck’s novel "Sweet Thursday," known for her independent spirit and evolving relationship with Doc in the Cannery Row community.
 E1202931 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: Suzy | Statement: [Sweet Thursday, mainCharacter, Suzy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Suzy
Context triple: [Sweet Thursday, mainCharacter, Suzy]
  • A. Suzy
    Suzy is a fictional character from the film "Cashback," portrayed by actress Michelle Ryan.
  • B. Suzie
    Suzie is a brilliant, tech-savvy girl from Stranger Things who helps Dustin Henderson and his friends by providing crucial scientific and hacking assistance.
  • C. Suze
    The Suze is a river in western Switzerland that flows through the Jura region and the city of Biel/Bienne before emptying into Lake Biel.
  • D. Suze
    Suze is the nickname of Suze Rotolo, an American artist and political activist best known for her relationship with Bob Dylan in the early 1960s.
  • E. Susie
    Susie is a common diminutive or nickname for the female given name Susanna.
  • 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: Suzy
Triple: [Sweet Thursday, mainCharacter, Suzy]
Generated description
Suzy is the central female protagonist of John Steinbeck’s novel "Sweet Thursday," known for her independent spirit and evolving relationship with Doc in the Cannery Row community.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Suzy
Target entity description: Suzy is the central female protagonist of John Steinbeck’s novel "Sweet Thursday," known for her independent spirit and evolving relationship with Doc in the Cannery Row community.
  • A. Suzy
    Suzy is a fictional character from the film "Cashback," portrayed by actress Michelle Ryan.
  • B. Suzie
    Suzie is a brilliant, tech-savvy girl from Stranger Things who helps Dustin Henderson and his friends by providing crucial scientific and hacking assistance.
  • C. Suze
    The Suze is a river in western Switzerland that flows through the Jura region and the city of Biel/Bienne before emptying into Lake Biel.
  • D. Suze
    Suze is the nickname of Suze Rotolo, an American artist and political activist best known for her relationship with Bob Dylan in the early 1960s.
  • E. Susie
    Susie is a common diminutive or nickname for the female given name Susanna.
  • 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_69d87f2171208190951025e526947816 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24597b74481908fdb8175628a57a1 completed April 17, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a000ee788f88190b16d267f1eee6d62 completed May 10, 2026, 4:51 a.m.
NEDg Description generation batch_6a00113900c88190bf7f56ca4b16a84c completed May 10, 2026, 5:01 a.m.
NED2 Entity disambiguation (via description) batch_6a0011d98f708190805c84d63ed79aaa completed May 10, 2026, 5:04 a.m.
Created at: April 10, 2026, 5:04 a.m.