Triple

T2415146
Position Surface form Disambiguated ID Type / Status
Subject The Good, the Bad and the Ugly E52284 entity
Predicate character P662 FINISHED
Object Tuco
Tuco is a cunning, comically talkative Mexican bandit and one of the three central gunslingers in the classic Spaghetti Western film "The Good, the Bad and the Ugly."
 E265018 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: Tuco | Statement: [The Good, the Bad and the Ugly, character, Tuco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tuco
Context triple: [The Good, the Bad and the Ugly, character, Tuco]
  • A. Hondo
    Hondo is the nickname of John Havlicek, a Hall of Fame Boston Celtics swingman renowned for his versatility, stamina, and clutch performances in the NBA.
  • B. Diego
    Diego is a given name of Spanish origin commonly used in Spanish-speaking countries and beyond.
  • C. Lalo
    Lalo is a common Spanish nickname for the given name Eduardo.
  • D. Dr. King Schultz
    Dr. King Schultz is a charismatic German bounty hunter and former dentist who becomes Django’s mentor and partner in Quentin Tarantino’s film "Django Unchained."
  • E. Sergeant Gonzales
    Sergeant Gonzales is a blustery, often comic Spanish soldier who serves as one of the primary antagonists to the masked hero Zorro in "The Mark of Zorro."
  • 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: Tuco
Triple: [The Good, the Bad and the Ugly, character, Tuco]
Generated description
Tuco is a cunning, comically talkative Mexican bandit and one of the three central gunslingers in the classic Spaghetti Western film "The Good, the Bad and the Ugly."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tuco
Target entity description: Tuco is a cunning, comically talkative Mexican bandit and one of the three central gunslingers in the classic Spaghetti Western film "The Good, the Bad and the Ugly."
  • A. Hondo
    Hondo is the nickname of John Havlicek, a Hall of Fame Boston Celtics swingman renowned for his versatility, stamina, and clutch performances in the NBA.
  • B. Diego
    Diego is a given name of Spanish origin commonly used in Spanish-speaking countries and beyond.
  • C. Lalo
    Lalo is a common Spanish nickname for the given name Eduardo.
  • D. Dr. King Schultz
    Dr. King Schultz is a charismatic German bounty hunter and former dentist who becomes Django’s mentor and partner in Quentin Tarantino’s film "Django Unchained."
  • E. Sergeant Gonzales
    Sergeant Gonzales is a blustery, often comic Spanish soldier who serves as one of the primary antagonists to the masked hero Zorro in "The Mark of Zorro."
  • 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_69ab495622948190bc6bc6e4cddaf645 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc94bd7ec81909f5b4a16a406165b completed March 7, 2026, 6:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf4748d0819088aec3bc14dc5519 completed March 9, 2026, 12:38 p.m.
NEDg Description generation batch_69aec338adf481908492b99d6e949bf8 completed March 9, 2026, 12:55 p.m.
NED2 Entity disambiguation (via description) batch_69aec3d410048190b908e883442b4ac2 completed March 9, 2026, 12:57 p.m.
Created at: March 6, 2026, 9:41 p.m.