Triple

T14275249
Position Surface form Disambiguated ID Type / Status
Subject Tess Daly E353899 entity
Predicate nickname P55 FINISHED
Object Tess
Tess is a British television presenter and model best known for co-hosting the BBC One dance competition show "Strictly Come Dancing."
 E1090546 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: Tess | Statement: [Tess Daly, nickname, Tess]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tess
Context triple: [Tess Daly, nickname, Tess]
  • A. Tess
    Tess is a central character in the musical film "Burlesque," serving as the tough but caring owner and manager of the struggling burlesque club.
  • B. Tess
    Tess is a 1979 period drama film directed by Roman Polanski, adapted from Thomas Hardy’s novel "Tess of the d'Urbervilles."
  • C. Tess
    Tess is a character in the action film "Fast X," part of the long-running Fast & Furious franchise.
  • D. Tess
    Tess is a central angelic character from the television series "Touched by an Angel," known for her wise, no-nonsense guidance to both humans and fellow angels.
  • E. Tess of the D’Urbervilles
    Tess of the D’Urbervilles is a classic 1891 novel by Thomas Hardy that follows the tragic life of Tess Durbeyfield, a young woman struggling against social injustice, fate, and moral hypocrisy in rural Victorian England.
  • 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: Tess
Triple: [Tess Daly, nickname, Tess]
Generated description
Tess is a British television presenter and model best known for co-hosting the BBC One dance competition show "Strictly Come Dancing."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tess
Target entity description: Tess is a British television presenter and model best known for co-hosting the BBC One dance competition show "Strictly Come Dancing."
  • A. Tess
    Tess is a central character in the musical film "Burlesque," serving as the tough but caring owner and manager of the struggling burlesque club.
  • B. Tess
    Tess is a 1979 period drama film directed by Roman Polanski, adapted from Thomas Hardy’s novel "Tess of the d'Urbervilles."
  • C. Tess
    Tess is a character in the action film "Fast X," part of the long-running Fast & Furious franchise.
  • D. Tess
    Tess is a central angelic character from the television series "Touched by an Angel," known for her wise, no-nonsense guidance to both humans and fellow angels.
  • E. Tess of the D’Urbervilles
    Tess of the D’Urbervilles is a classic 1891 novel by Thomas Hardy that follows the tragic life of Tess Durbeyfield, a young woman struggling against social injustice, fate, and moral hypocrisy in rural Victorian England.
  • 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_69d8278d25148190abf1a8c8f5f533ad completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6582f5308190969f4cfd724d9139 completed April 14, 2026, 4:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd326d35808190bbf3f6bbc50554f4 completed May 8, 2026, 12:46 a.m.
NEDg Description generation batch_69fd372c49d88190ad76477d24e48d59 completed May 8, 2026, 1:06 a.m.
NED2 Entity disambiguation (via description) batch_69fd379feff081908a74d12782bedbee completed May 8, 2026, 1:08 a.m.
Created at: April 10, 2026, 1:10 a.m.