Triple

T159714
Position Surface form Disambiguated ID Type / Status
Subject Dave Martinez E3255 entity
Predicate familyName P18 FINISHED
Object Martinez
Martinez is a common Spanish-origin surname widely borne across the Spanish-speaking world and beyond.
 E28750 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: Martinez | Statement: [Dave Martinez, familyName, Martinez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martinez
Context triple: [Dave Martinez, familyName, Martinez]
  • A. Vallejo
    Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
  • B. San Antonio de los Baños
    San Antonio de los Baños is a Cuban town known for its film school and cultural traditions, located southwest of Havana.
  • C. San Carlos
    San Carlos is a city in San Mateo County, California, located on the San Francisco Peninsula between Belmont and Redwood City.
  • D. Chico
    Chico is a mid-sized city in Northern California known for California State University, Chico, and its large urban park, Bidwell Park.
  • E. San Borja
    San Borja is a primarily residential and commercial district in Lima, Peru, known for its middle- to upper-class neighborhoods, green areas, and cultural institutions.
  • 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: Martinez
Triple: [Dave Martinez, familyName, Martinez]
Generated description
Martinez is a common Spanish-origin surname widely borne across the Spanish-speaking world and beyond.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Martinez
Target entity description: Martinez is a common Spanish-origin surname widely borne across the Spanish-speaking world and beyond.
  • A. Vallejo
    Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
  • B. San Antonio de los Baños
    San Antonio de los Baños is a Cuban town known for its film school and cultural traditions, located southwest of Havana.
  • C. San Carlos
    San Carlos is a city in San Mateo County, California, located on the San Francisco Peninsula between Belmont and Redwood City.
  • D. Chico
    Chico is a mid-sized city in Northern California known for California State University, Chico, and its large urban park, Bidwell Park.
  • E. San Borja
    San Borja is a primarily residential and commercial district in Lima, Peru, known for its middle- to upper-class neighborhoods, green areas, and cultural institutions.
  • 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_69a2527757ec819090b8becb2cf1a862 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a25855baf48190a1b63f2e5865d957 completed Feb. 28, 2026, 2:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69a352734730819091211462a23204ea completed Feb. 28, 2026, 8:39 p.m.
NEDg Description generation batch_69a355f97db8819080665fa585955380 completed Feb. 28, 2026, 8:54 p.m.
NED2 Entity disambiguation (via description) batch_69a3567126dc81909853cb7dab5609c1 completed Feb. 28, 2026, 8:56 p.m.
Created at: Feb. 28, 2026, 2:31 a.m.