Triple

T5129405
Position Surface form Disambiguated ID Type / Status
Subject Yaphet Kotto E115658 entity
Predicate spouse P13 FINISHED
Object Tessie Sinahon
Tessie Sinahon is the widow of American actor Yaphet Kotto, known for being his longtime partner and surviving spouse.
 E496053 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: Tessie Sinahon | Statement: [Yaphet Kotto, spouse, Tessie Sinahon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tessie Sinahon
Context triple: [Yaphet Kotto, spouse, Tessie Sinahon]
  • A. Tess Sanchez
    Tess Sanchez is an American casting director and former head of casting at Fox, known for her work on numerous television series.
  • B. Helen Lasichanh
    Helen Lasichanh is a Laotian-Ethiopian model, designer, and stylist known for her distinctive fashion sense and marriage to musician Pharrell Williams.
  • C. Marlene Mathias
    Marlene Mathias is known as the daughter of American Olympic decathlon champion and politician Bob Mathias.
  • D. Felicia Hano
    Felicia Hano is an American artistic gymnast and former elite competitor who became a standout collegiate gymnast for the UCLA Bruins.
  • E. Ethel "Sunshine" Akalino
    Ethel "Sunshine" Akalino is a fictional character from the 1970s American sitcom "Blansky's Beauties," known as one of the young showgirls managed by the title character Nancy Blansky.
  • 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: Tessie Sinahon
Triple: [Yaphet Kotto, spouse, Tessie Sinahon]
Generated description
Tessie Sinahon is the widow of American actor Yaphet Kotto, known for being his longtime partner and surviving spouse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tessie Sinahon
Target entity description: Tessie Sinahon is the widow of American actor Yaphet Kotto, known for being his longtime partner and surviving spouse.
  • A. Tess Sanchez
    Tess Sanchez is an American casting director and former head of casting at Fox, known for her work on numerous television series.
  • B. Helen Lasichanh
    Helen Lasichanh is a Laotian-Ethiopian model, designer, and stylist known for her distinctive fashion sense and marriage to musician Pharrell Williams.
  • C. Marlene Mathias
    Marlene Mathias is known as the daughter of American Olympic decathlon champion and politician Bob Mathias.
  • D. Felicia Hano
    Felicia Hano is an American artistic gymnast and former elite competitor who became a standout collegiate gymnast for the UCLA Bruins.
  • E. Ethel "Sunshine" Akalino
    Ethel "Sunshine" Akalino is a fictional character from the 1970s American sitcom "Blansky's Beauties," known as one of the young showgirls managed by the title character Nancy Blansky.
  • 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_69bd444426bc819099ccd23f141e22aa completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7825facc8190b2a6c17216290b5c completed March 20, 2026, 4:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69bec4c23d5c8190883a297254d9c80d completed March 21, 2026, 4:18 p.m.
NEDg Description generation batch_69bec6620aac8190a820190e7facd70a completed March 21, 2026, 4:25 p.m.
NED2 Entity disambiguation (via description) batch_69bec70062f48190baae277e6f8c5c4e completed March 21, 2026, 4:27 p.m.
Created at: March 20, 2026, 1:42 p.m.