Triple

T4170174
Position Surface form Disambiguated ID Type / Status
Subject Fawlty Towers E84543 entity
Predicate mainCharacter P1183 FINISHED
Object Manuel
Manuel is the hapless, linguistically challenged Spanish waiter from the British sitcom "Fawlty Towers," known for his comedic misunderstandings and clashes with Basil Fawlty.
 E424281 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: Manuel | Statement: [Fawlty Towers, mainCharacter, Manuel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Manuel
Context triple: [Fawlty Towers, mainCharacter, Manuel]
  • A. Manuel
    Manuel is the given name of Manny Ramirez, the former Major League Baseball star known for his powerful hitting and tenure with the Boston Red Sox.
  • B. Francisco
    Francisco is a masculine given name of Spanish and Portuguese origin, equivalent to Francis in English.
  • C. Fernando
    "Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
  • D. Fernando
    Fernando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking and Lusophone countries.
  • E. Fernando
    Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
  • 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: Manuel
Triple: [Fawlty Towers, mainCharacter, Manuel]
Generated description
Manuel is the hapless, linguistically challenged Spanish waiter from the British sitcom "Fawlty Towers," known for his comedic misunderstandings and clashes with Basil Fawlty.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Manuel
Target entity description: Manuel is the hapless, linguistically challenged Spanish waiter from the British sitcom "Fawlty Towers," known for his comedic misunderstandings and clashes with Basil Fawlty.
  • A. Manuel
    Manuel is the given name of Manny Ramirez, the former Major League Baseball star known for his powerful hitting and tenure with the Boston Red Sox.
  • B. Francisco
    Francisco is a masculine given name of Spanish and Portuguese origin, equivalent to Francis in English.
  • C. Fernando
    "Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
  • D. Fernando
    Fernando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking and Lusophone countries.
  • E. Fernando
    Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
  • 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_69aed932cab48190b80ffe35f7029ae1 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af02c87cc88190a9ec3712db18a8a7 completed March 9, 2026, 5:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a84713f88190a051b7d94b89d585 completed March 14, 2026, 6:26 p.m.
NEDg Description generation batch_69b5ac38488481908e357c35fbdcbc60 completed March 14, 2026, 6:43 p.m.
NED2 Entity disambiguation (via description) batch_69b5acc576308190a5e34b580db46944 completed March 14, 2026, 6:45 p.m.
Created at: March 9, 2026, 3:44 p.m.