Triple

T15834102
Position Surface form Disambiguated ID Type / Status
Subject Tony Xu E383942 entity
Predicate givenName P17 FINISHED
Object Tony
Tony is a common masculine given name, often used as a diminutive of Anthony or Antonio in English-speaking and other cultures.
 E265285 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: Tony | Statement: [Tony Xu, givenName, Tony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tony
Context triple: [Tony Xu, givenName, Tony]
  • A. Tony
    Tony is the central romantic lead in the musical "The Most Happy Fella," an aging Italian-American vintner whose love story drives the plot.
  • B. Tony
    Tony is the humanoid robot protagonist of Isaac Asimov’s science fiction short story “Satisfaction Guaranteed,” designed to interact closely with humans and explore the emotional and ethical implications of human–robot relationships.
  • C. Tony
    Tony is a fictional character appearing in the Marx Brothers comedy film "A Day at the Races."
  • D. Tony
    Tony is a kind-hearted Chicago police officer who briefly dates Fiona Gallagher in the U.S. version of the TV series "Shameless."
  • E. Tony
    Tony is a fictional character appearing in the romantic comedy film "Come September."
  • 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: Tony
Triple: [Tony Xu, givenName, Tony]
Generated description
Tony is a common masculine given name, often used as a diminutive of Anthony or Antonio in English-speaking and other cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tony
Target entity description: Tony is a common masculine given name, often used as a diminutive of Anthony or Antonio in English-speaking and other cultures.
  • A. Tony chosen
    Tony is a common masculine given name, often used as a diminutive of Anthony or Antonio.
  • B. Tony
    Tony is the NATO reporting name for the Japanese World War II Kawasaki Ki-61 fighter aircraft.
  • C. Tony
    Tony is the central romantic lead in the musical "The Most Happy Fella," an aging Italian-American vintner whose love story drives the plot.
  • D. Tony
    Tony is the central male protagonist in the play and film "They Knew What They Wanted," an aging Italian-American winegrower whose impulsive mail-order marriage drives the story’s romantic and dramatic conflicts.
  • E. Tony
    Tony is the idealistic young protagonist of the musical *West Side Story*, whose forbidden love for Maria drives the story’s modern retelling of *Romeo and Juliet* in 1950s New York.
  • F. None of above.

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_69d86da34c888190976e06c4019d415a completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e11e6670d48190a456581dd951f168 completed April 16, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa137be2c81909c8f04b5cc1a5b21 completed May 9, 2026, 9:03 p.m.
NEDg Description generation batch_69ffa527af048190b1f87d85e50bf254 completed May 9, 2026, 9:20 p.m.
NED2 Entity disambiguation (via description) batch_69ffa5df00e481909e203e78940395ed completed May 9, 2026, 9:23 p.m.
Created at: April 10, 2026, 4:49 a.m.