Triple

T1857322
Position Surface form Disambiguated ID Type / Status
Subject Juan de Lángara E41732 entity
Predicate familyName P18 FINISHED
Object de Lángara
de Lángara is a Spanish surname most notably associated with the 18th-century admiral Juan de Lángara, a prominent naval commander in the Spanish Navy.
 E207912 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 Lángara | Statement: [Juan de Lángara, familyName, de Lángara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: de Lángara
Context triple: [Juan de Lángara, familyName, de Lángara]
  • A. Saclan
    Saclan is a now-extinct Miwok language once spoken by Indigenous people in what is now central California.
  • B. Strathearn
    Strathearn is a scenic valley region in central Scotland, known for its rural landscapes, historic towns, and the River Earn running through it.
  • C. Kierling
    Kierling is a small locality in Lower Austria best known as the place where writer Franz Kafka spent his final days and died.
  • D. Lorne
    Lorne is a masculine given name most notably associated with Canadian-American television producer and "Saturday Night Live" creator Lorne Michaels.
  • E. Whistler
    Whistler was the internal codename used by Microsoft during the development of the Windows XP operating system.
  • 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 Lángara
Triple: [Juan de Lángara, familyName, de Lángara]
Generated description
de Lángara is a Spanish surname most notably associated with the 18th-century admiral Juan de Lángara, a prominent naval commander in the Spanish Navy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: de Lángara
Target entity description: de Lángara is a Spanish surname most notably associated with the 18th-century admiral Juan de Lángara, a prominent naval commander in the Spanish Navy.
  • A. Saclan
    Saclan is a now-extinct Miwok language once spoken by Indigenous people in what is now central California.
  • B. Strathearn
    Strathearn is a scenic valley region in central Scotland, known for its rural landscapes, historic towns, and the River Earn running through it.
  • C. Kierling
    Kierling is a small locality in Lower Austria best known as the place where writer Franz Kafka spent his final days and died.
  • D. Lorne
    Lorne is a masculine given name most notably associated with Canadian-American television producer and "Saturday Night Live" creator Lorne Michaels.
  • E. Whistler
    Whistler was the internal codename used by Microsoft during the development of the Windows XP operating system.
  • 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_69a8864a83848190a4ec02721306c511 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb07fc5f08190a195a2f24d7b858a completed March 7, 2026, 4:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1cb5b708190a0b89b157ea9da58 completed March 8, 2026, 7:45 p.m.
NEDg Description generation batch_69add2729498819082f6595c57f545a8 completed March 8, 2026, 7:48 p.m.
NED2 Entity disambiguation (via description) batch_69add32c32b08190be6624eefa2fa386 completed March 8, 2026, 7:51 p.m.
Created at: March 4, 2026, 7:33 p.m.