Triple

T3065320
Position Surface form Disambiguated ID Type / Status
Subject Silao, Guanajuato, Mexico E62090 entity
Predicate hasNameInLanguage P15 FINISHED
Object Silao (Spanish)
Silao (Spanish) refers to the city of Silao, an important industrial and transportation hub located in the state of Guanajuato, Mexico.
 E323580 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: Silao (Spanish) | Statement: [Silao, Guanajuato, Mexico, hasNameInLanguage, Silao (Spanish)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Silao (Spanish)
Context triple: [Silao, Guanajuato, Mexico, hasNameInLanguage, Silao (Spanish)]
  • A. Molinero (Spanish)
    Molinero is a Spanish occupational surname meaning "miller," equivalent to the German surname Müller (Mueller).
  • B. Loiceño
    Loiceño is the Spanish demonym for a person from the municipality of Loíza in Puerto Rico.
  • C. Masbateño
    Masbateño is a Central Philippine Bisayan language spoken primarily on Masbate Island in the Philippines.
  • D. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • E. Tasqueña
    Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
  • 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: Silao (Spanish)
Triple: [Silao, Guanajuato, Mexico, hasNameInLanguage, Silao (Spanish)]
Generated description
Silao (Spanish) refers to the city of Silao, an important industrial and transportation hub located in the state of Guanajuato, Mexico.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Silao (Spanish)
Target entity description: Silao (Spanish) refers to the city of Silao, an important industrial and transportation hub located in the state of Guanajuato, Mexico.
  • A. Molinero (Spanish)
    Molinero is a Spanish occupational surname meaning "miller," equivalent to the German surname Müller (Mueller).
  • B. Loiceño
    Loiceño is the Spanish demonym for a person from the municipality of Loíza in Puerto Rico.
  • C. Masbateño
    Masbateño is a Central Philippine Bisayan language spoken primarily on Masbate Island in the Philippines.
  • D. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • E. Tasqueña
    Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
  • 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada0fc01dc81908fbdf7c1ef73afe4 completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1ef1402108190a2d24e7eb523f658 completed March 11, 2026, 10:39 p.m.
NEDg Description generation batch_69b1f2f3d120819090d28e0353d3d8da completed March 11, 2026, 10:55 p.m.
NED2 Entity disambiguation (via description) batch_69b1f365a4988190ae3ea6370a27ee72 completed March 11, 2026, 10:57 p.m.
Created at: March 8, 2026, 3:02 p.m.