Triple

T710742
Position Surface form Disambiguated ID Type / Status
Subject Marcelo Caetano E14200 entity
Predicate givenName P17 FINISHED
Object 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.
 E103767 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: Marcelo | Statement: [Marcelo Caetano, givenName, Marcelo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marcelo
Context triple: [Marcelo Caetano, givenName, Marcelo]
  • A. Jorge
    Jorge is the birth name of Pope Francis, the head of the Roman Catholic Church and the first pope from the Americas.
  • B. Sebastián
    Sebastián is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
  • C. Raúl
    Raúl is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
  • D. Eduardo
    Eduardo is a masculine given name commonly used in Spanish and Portuguese-speaking countries, equivalent to the English name Edward.
  • E. Roberto
    Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
  • 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: Marcelo
Triple: [Marcelo Caetano, givenName, Marcelo]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marcelo
Target entity description: 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.
  • A. Jorge
    Jorge is the birth name of Pope Francis, the head of the Roman Catholic Church and the first pope from the Americas.
  • B. Sebastián
    Sebastián is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
  • C. Raúl
    Raúl is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
  • D. Eduardo
    Eduardo is a masculine given name commonly used in Spanish and Portuguese-speaking countries, equivalent to the English name Edward.
  • E. Roberto
    Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
  • 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_69a4934a36e081909e7abef98b898a4e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a55c99fc8190941c5fd18551792a completed March 1, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7b83709248190bee17ec028b12bae completed March 4, 2026, 4:42 a.m.
NEDg Description generation batch_69a7b93aed5c8190b5a588ed4a5eb94d completed March 4, 2026, 4:46 a.m.
NED2 Entity disambiguation (via description) batch_69a7b9bb13f08190ad75518ba81b210d completed March 4, 2026, 4:48 a.m.
Created at: March 1, 2026, 7:36 p.m.