Triple

T6038605
Position Surface form Disambiguated ID Type / Status
Subject Jacqueline E134483 entity
Predicate cognateOf P8954 FINISHED
Object Jacobina
Jacobina is a feminine given name, primarily used in Germanic and Scandinavian contexts, that is etymologically related to names like Jacqueline and Jacob.
 E563732 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: Jacobina | Statement: [Jacqueline, cognateOf, Jacobina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jacobina
Context triple: [Jacqueline, cognateOf, Jacobina]
  • A. Yelinda
    Yelinda is a dialect of the Bulu language spoken by a specific subgroup of Bulu speakers in Cameroon.
  • B. Eugenia
    Eugenia is a feminine given name of Greek origin, commonly used in various European and Latin American cultures.
  • C. Maiana
    Maiana is a low-lying coral atoll in the central Pacific nation of Kiribati, known for its traditional village life and vulnerability to sea-level rise.
  • D. Joaquina
    Joaquina is the given name of Infanta Carlota Joaquina of Spain, a Spanish-born princess who became Queen consort of Portugal.
  • E. Jaqueira
    Jaqueira is a neighborhood in Recife, Brazil, known for its large urban park and residential character.
  • 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: Jacobina
Triple: [Jacqueline, cognateOf, Jacobina]
Generated description
Jacobina is a feminine given name, primarily used in Germanic and Scandinavian contexts, that is etymologically related to names like Jacqueline and Jacob.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jacobina
Target entity description: Jacobina is a feminine given name, primarily used in Germanic and Scandinavian contexts, that is etymologically related to names like Jacqueline and Jacob.
  • A. Yelinda
    Yelinda is a dialect of the Bulu language spoken by a specific subgroup of Bulu speakers in Cameroon.
  • B. Eugenia
    Eugenia is a feminine given name of Greek origin, commonly used in various European and Latin American cultures.
  • C. Maiana
    Maiana is a low-lying coral atoll in the central Pacific nation of Kiribati, known for its traditional village life and vulnerability to sea-level rise.
  • D. Joaquina
    Joaquina is the given name of Infanta Carlota Joaquina of Spain, a Spanish-born princess who became Queen consort of Portugal.
  • E. Jaqueira
    Jaqueira is a neighborhood in Recife, Brazil, known for its large urban park and residential character.
  • 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_69c00875db5c819099dd5bb833ec43c2 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c056ccac948190a27547878d4db8e4 completed March 22, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1139031248190b796a655bf07a4bc completed March 23, 2026, 10:18 a.m.
NEDg Description generation batch_69c11400ddf08190b99943ada6ff2703 completed March 23, 2026, 10:20 a.m.
NED2 Entity disambiguation (via description) batch_69c1147e55fc81909225e1fe9e3eeec4 completed March 23, 2026, 10:22 a.m.
Created at: March 22, 2026, 4:08 p.m.