Triple

T1915579
Position Surface form Disambiguated ID Type / Status
Subject Iris Steensma E40008 entity
Predicate appearsAlongsideCharacter P25756 FINISHED
Object Betsy
Betsy is a character from the film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
 E141534 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: Betsy | Statement: [Iris Steensma, appearsAlongsideCharacter, Betsy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Betsy
Context triple: [Iris Steensma, appearsAlongsideCharacter, Betsy]
  • A. Betsy
    Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
  • B. Martha
    Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
  • C. Mary Ann
    Mary Ann is the namesake of the city of Marianna in Florida.
  • D. Abigail
    Abigail is a feminine given name of Hebrew origin meaning "my father is joy," historically popular in English-speaking countries.
  • E. Betty
    Betty is a minor character in Enid Blyton’s "Malory Towers" series, known as a lively and mischievous schoolgirl at the boarding school.
  • 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: Betsy
Triple: [Iris Steensma, appearsAlongsideCharacter, Betsy]
Generated description
Betsy is a character from the film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Betsy
Target entity description: Betsy is a character from the film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
  • A. Betsy chosen
    Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
  • B. Martha
    Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
  • C. Mary Ann
    Mary Ann is the namesake of the city of Marianna in Florida.
  • D. Abigail
    Abigail is a feminine given name of Hebrew origin meaning "my father is joy," historically popular in English-speaking countries.
  • E. Betty
    Betty is a minor character in Enid Blyton’s "Malory Towers" series, known as a lively and mischievous schoolgirl at the boarding school.
  • F. None of above.

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_69a8864298748190a2f2fd34f7ef8d77 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb7c51c2881908054760c624dd577 completed March 7, 2026, 5:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3da81308190a49844a8ac2997da completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf5d259488190ad105f0083577ecc completed March 8, 2026, 10:18 p.m.
NED2 Entity disambiguation (via description) batch_69adf6264d148190be0daef2be7f920d completed March 8, 2026, 10:20 p.m.
Created at: March 4, 2026, 7:35 p.m.