Triple

T18332245
Position Surface form Disambiguated ID Type / Status
Subject Harriet de Onís E439173 entity
Predicate familyName P18 FINISHED
Object de Onís
de Onís is the surname of Harriet de Onís, an American translator known for bringing major works of Latin American literature into English.
 E1318219 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: de Onís | Statement: [Harriet de Onís, familyName, de Onís]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: de Onís
Context triple: [Harriet de Onís, familyName, de Onís]
  • A. Yussois
    Yussois is the French demonym for inhabitants of the town of Yutz in northeastern France.
  • B. Resaena
    Resaena was an ancient city in northern Mesopotamia, strategically located on key trade and military routes between the Roman and Persian empires.
  • C. Diogu
    Diogu is the surname of Ike Diogu, a Nigerian-American professional basketball player known for his career in the NBA and international leagues.
  • D. La Senoge
    La Senoge is a small river in the canton of Vaud, Switzerland, that flows through the Jura region before joining the larger La Venoge.
  • E. Suseo
    Suseo is a neighborhood in southeastern Seoul, South Korea, known for its major high-speed rail station that links the capital to cities such as Busan.
  • 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: de Onís
Triple: [Harriet de Onís, familyName, de Onís]
Generated description
de Onís is the surname of Harriet de Onís, an American translator known for bringing major works of Latin American literature into English.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: de Onís
Target entity description: de Onís is the surname of Harriet de Onís, an American translator known for bringing major works of Latin American literature into English.
  • A. Yussois
    Yussois is the French demonym for inhabitants of the town of Yutz in northeastern France.
  • B. Resaena
    Resaena was an ancient city in northern Mesopotamia, strategically located on key trade and military routes between the Roman and Persian empires.
  • C. Diogu
    Diogu is the surname of Ike Diogu, a Nigerian-American professional basketball player known for his career in the NBA and international leagues.
  • D. La Senoge
    La Senoge is a small river in the canton of Vaud, Switzerland, that flows through the Jura region before joining the larger La Venoge.
  • E. Suseo
    Suseo is a neighborhood in southeastern Seoul, South Korea, known for its major high-speed rail station that links the capital to cities such as Busan.
  • 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_69d8b9175fec8190af865699b4e64d8c completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e50ecaf6f48190ae7547cc0f8e6efa completed April 19, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03c4d085748190bfd9a7e94f1344a5 completed May 13, 2026, 12:24 a.m.
NEDg Description generation batch_6a03c570ed348190a7e19eed29b06909 completed May 13, 2026, 12:27 a.m.
NED2 Entity disambiguation (via description) batch_6a03c5ec00a88190a93c3be5dd3bc6ee completed May 13, 2026, 12:29 a.m.
Created at: April 10, 2026, 10:36 a.m.