Triple

T4680514
Position Surface form Disambiguated ID Type / Status
Subject Miguel Topete E103786 entity
Predicate familyName P18 FINISHED
Object Topete E103786 NE FINISHED

How this triple was built (2 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: Topete | Statement: [Miguel Topete, familyName, Topete]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Topete
Context triple: [Miguel Topete, familyName, Topete]
  • A. Topete chosen
    Topete is a Spanish surname associated with notable figures such as admiral Pascual Cervera y Topete.
  • B. San Luis Talpa
    San Luis Talpa is a municipality in the La Paz department of El Salvador, located near the country’s main international airport and known for its role as a transit and service hub for travelers.
  • C. Tepatitlán de Morelos
    Tepatitlán de Morelos is a prominent city in Mexico known for its agricultural production, religious traditions, and vibrant regional culture.
  • D. Parras de la Fuente
    Parras de la Fuente is a historic town in northern Mexico renowned as one of the country’s oldest wine-producing regions and a notable center of viticulture in Coahuila.
  • E. San Pedro de los Pinos
    San Pedro de los Pinos is a traditional residential neighborhood in Mexico City known for its central location, local markets, and mix of historic and modern urban development.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd43debbf08190b4bc372e286ec234 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd636d306081908ff512896f54cb10 completed March 20, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69be67b720a481909cea26f0a8662bb0 completed March 21, 2026, 9:41 a.m.
Created at: March 20, 2026, 1:16 p.m.