Triple

T1321504
Position Surface form Disambiguated ID Type / Status
Subject Anton E28227 entity
Predicate hasDiminutive P456 FINISHED
Object Toni
Toni is a common diminutive given name, typically used as a shorter or more familiar form of names like Anton, Anthony, or Antonia.
 E150885 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: Toni | Statement: [Anton, hasDiminutive, Toni]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Toni
Context triple: [Anton, hasDiminutive, Toni]
  • A. 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.
  • B. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • C. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • D. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • E. Sonia
    Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
  • 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: Toni
Triple: [Anton, hasDiminutive, Toni]
Generated description
Toni is a common diminutive given name, typically used as a shorter or more familiar form of names like Anton, Anthony, or Antonia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Toni
Target entity description: Toni is a common diminutive given name, typically used as a shorter or more familiar form of names like Anton, Anthony, or Antonia.
  • A. 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.
  • B. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • C. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • D. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • E. Sonia
    Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
  • 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_69a498540a2481909e807a762280d3ba completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c19932888190a3d45871e84f112e completed March 1, 2026, 10:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbaf6a6d08190b8a30c2c64f15f59 completed March 7, 2026, 11:55 p.m.
NEDg Description generation batch_69acbbd8b6b881908412e5ab5d9baf79 completed March 7, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_69acbd0dcf74819093b5985b0f3bc8a2 completed March 8, 2026, 12:04 a.m.
Created at: March 1, 2026, 7:55 p.m.