Triple

T6891201
Position Surface form Disambiguated ID Type / Status
Subject The Gondoliers E159048 entity
Predicate hasCharacter P2308 FINISHED
Object Luiz
Luiz is a character in Gilbert and Sullivan's comic opera "The Gondoliers," serving as a romantic figure entangled in the opera's mistaken-identity and royal-intrigue plot.
 E628812 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: Luiz | Statement: [The Gondoliers, hasCharacter, Luiz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Luiz
Context triple: [The Gondoliers, hasCharacter, Luiz]
  • A. Luiz
    Luiz is a given name associated with the German novelist Heinrich Mann.
  • B. Marcelo
    Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
  • C. Guilherme
    Guilherme is the Portuguese form of the given name William, commonly used in Portuguese-speaking countries.
  • D. Gilberto
    Gilberto is a masculine given name of Romance-language origin, commonly used in Italian, Spanish, and Portuguese-speaking countries.
  • E. Eduardo Nery
    Eduardo Nery was a Portuguese artist best known for his innovative tilework and public art installations that blend geometric abstraction with architectural spaces.
  • 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: Luiz
Triple: [The Gondoliers, hasCharacter, Luiz]
Generated description
Luiz is a character in Gilbert and Sullivan's comic opera "The Gondoliers," serving as a romantic figure entangled in the opera's mistaken-identity and royal-intrigue plot.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Luiz
Target entity description: Luiz is a character in Gilbert and Sullivan's comic opera "The Gondoliers," serving as a romantic figure entangled in the opera's mistaken-identity and royal-intrigue plot.
  • A. Luiz
    Luiz is a given name associated with the German novelist Heinrich Mann.
  • B. Marcelo
    Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
  • C. Guilherme
    Guilherme is the Portuguese form of the given name William, commonly used in Portuguese-speaking countries.
  • D. Gilberto
    Gilberto is a masculine given name of Romance-language origin, commonly used in Italian, Spanish, and Portuguese-speaking countries.
  • E. Eduardo Nery
    Eduardo Nery was a Portuguese artist best known for his innovative tilework and public art installations that blend geometric abstraction with architectural spaces.
  • 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_69c6883568c8819081db6407e892cccc completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d92ecbdc8190992f9c7f4f33f4c4 completed March 27, 2026, 7:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7511fbe808190bc3dfb7c34a7cbb6 completed March 28, 2026, 3:55 a.m.
NEDg Description generation batch_69c7524d677c81909531ba9bb46f2632 completed March 28, 2026, 4 a.m.
NED2 Entity disambiguation (via description) batch_69c752bef2808190843f3cad53aa5702 completed March 28, 2026, 4:02 a.m.
Created at: March 27, 2026, 2:24 p.m.