Triple

T10553797
Position Surface form Disambiguated ID Type / Status
Subject Beauty Shop E249021 entity
Predicate character P662 FINISHED
Object Terri
Terri is a supporting character in the comedy film "Beauty Shop," contributing to the ensemble of stylists and clients at the salon.
 E870337 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: Terri | Statement: [Beauty Shop, character, Terri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Terri
Context triple: [Beauty Shop, character, Terri]
  • A. Terri
    Terri is a common diminutive form of the given name Theresa.
  • B. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • C. Teri
    Teri is a central character in the film and television series "Soul Food," known as the ambitious, high-powered attorney whose strained relationships with her family drive much of the story’s drama.
  • D. Trisha
    Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
  • E. Lori
    Lori is a feminine given name commonly used in English-speaking countries, often as a diminutive of Laura or Lorraine.
  • 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: Terri
Triple: [Beauty Shop, character, Terri]
Generated description
Terri is a supporting character in the comedy film "Beauty Shop," contributing to the ensemble of stylists and clients at the salon.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Terri
Target entity description: Terri is a supporting character in the comedy film "Beauty Shop," contributing to the ensemble of stylists and clients at the salon.
  • A. Terri
    Terri is a common diminutive form of the given name Theresa.
  • B. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • C. Teri
    Teri is a central character in the film and television series "Soul Food," known as the ambitious, high-powered attorney whose strained relationships with her family drive much of the story’s drama.
  • D. Trisha
    Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
  • E. Lori
    Lori is a feminine given name commonly used in English-speaking countries, often as a diminutive of Laura or Lorraine.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d527118da081909ca61bc555a17609 completed April 7, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d9346f6a38819087647e7a09f40c41 completed April 10, 2026, 5:33 p.m.
NEDg Description generation batch_69d938c8b25c8190bb048053d8668e5c completed April 10, 2026, 5:52 p.m.
NED2 Entity disambiguation (via description) batch_69d939b1844881908c8fbcb9488863f6 completed April 10, 2026, 5:56 p.m.
Created at: April 6, 2026, 12:34 p.m.