Triple

T3332698
Position Surface form Disambiguated ID Type / Status
Subject The North Water E70068 entity
Predicate executiveProducer P7225 FINISHED
Object Tessa Ross
Tessa Ross is a prominent British film and television producer known for backing acclaimed, often auteur-driven projects across UK cinema and high-end TV drama.
 E449176 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: Tessa Ross | Statement: [The North Water, executiveProducer, Tessa Ross]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tessa Ross
Context triple: [The North Water, executiveProducer, Tessa Ross]
  • A. Tessa Berens
    Tessa Berens is a fictional character from the work titled "The Silence."
  • B. Shiri Appleby
    Shiri Appleby is an American actress best known for her lead role in the TV series "Roswell" and her later work on shows like "UnREAL."
  • C. Tahnee Welch
    Tahnee Welch is an American actress and model best known for her role in the science-fiction film "Cocoon" and for being the daughter of actress Raquel Welch.
  • D. Tessa Ensler
    Tessa Ensler is a character portrayed by Jodie Comer, likely in a dramatic screen or stage production.
  • E. Larissa Howard
    Larissa Howard is known as the daughter of British military historian and politician Michael Howard.
  • 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: Tessa Ross
Triple: [The North Water, executiveProducer, Tessa Ross]
Generated description
Tessa Ross is a prominent British film and television producer known for backing acclaimed, often auteur-driven projects across UK cinema and high-end TV drama.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tessa Ross
Target entity description: Tessa Ross is a prominent British film and television producer known for backing acclaimed, often auteur-driven projects across UK cinema and high-end TV drama.
  • A. Tessa Berens
    Tessa Berens is a fictional character from the work titled "The Silence."
  • B. Shiri Appleby
    Shiri Appleby is an American actress best known for her lead role in the TV series "Roswell" and her later work on shows like "UnREAL."
  • C. Tahnee Welch
    Tahnee Welch is an American actress and model best known for her role in the science-fiction film "Cocoon" and for being the daughter of actress Raquel Welch.
  • D. Tessa Ensler
    Tessa Ensler is a character portrayed by Jodie Comer, likely in a dramatic screen or stage production.
  • E. Larissa Howard
    Larissa Howard is known as the daughter of British military historian and politician Michael Howard.
  • 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_69ad85a24f208190bcf83131bfed3521 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb19358e48190a503af01b92273a4 completed March 8, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bda3f5a8a88190a494a9338c01962a completed March 20, 2026, 7:45 p.m.
NEDg Description generation batch_69bda554141c8190968f265727acf127 completed March 20, 2026, 7:51 p.m.
NED2 Entity disambiguation (via description) batch_69bda5bea35881908911e30c857c9844 completed March 20, 2026, 7:53 p.m.
Created at: March 8, 2026, 3:12 p.m.