Triple

T1802754
Position Surface form Disambiguated ID Type / Status
Subject Tamil cinema E39755 entity
Predicate hasNotableActress P17435 FINISHED
Object Trisha
Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
 E222184 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: Trisha | Statement: [Tamil cinema, hasNotableActress, Trisha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Trisha
Context triple: [Tamil cinema, hasNotableActress, Trisha]
  • A. Sandra
    Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
  • B. Marcia
    Marcia was the mother of the Roman emperor Trajan and a member of the provincial Roman aristocracy in Hispania.
  • C. Trudy
    Trudy is the nickname of Gertrude Ederle, the American competitive swimmer who became the first woman to swim across the English Channel.
  • D. Tina
    Tina is the nickname of Tina Fey, an American comedian, writer, actress, and producer best known for her work on Saturday Night Live and 30 Rock.
  • E. Bridgette
    Bridgette is a feminine given name commonly used in English-speaking countries, often considered a variant of "Bridget."
  • 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: Trisha
Triple: [Tamil cinema, hasNotableActress, Trisha]
Generated description
Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Trisha
Target entity description: Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
  • A. Sandra
    Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
  • B. Marcia
    Marcia was the mother of the Roman emperor Trajan and a member of the provincial Roman aristocracy in Hispania.
  • C. Trudy
    Trudy is the nickname of Gertrude Ederle, the American competitive swimmer who became the first woman to swim across the English Channel.
  • D. Tina
    Tina is the nickname of Tina Fey, an American comedian, writer, actress, and producer best known for her work on Saturday Night Live and 30 Rock.
  • E. Bridgette
    Bridgette is a feminine given name commonly used in English-speaking countries, often considered a variant of "Bridget."
  • 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_69a88632aa588190ba3978fde0db5bbd completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abaffee0f88190aa7a42ef4a4e2bd2 completed March 7, 2026, 4:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0305b6d08190804da5445e1f54d9 completed March 8, 2026, 11:15 p.m.
NEDg Description generation batch_69ae03b41dcc81909b4439006bdffc64 completed March 8, 2026, 11:18 p.m.
NED2 Entity disambiguation (via description) batch_69ae044314188190a7472cf5f8e89f6c completed March 8, 2026, 11:20 p.m.
Created at: March 4, 2026, 7:32 p.m.