Triple

T179739
Position Surface form Disambiguated ID Type / Status
Subject Sylvia Nasar E3657 entity
Predicate givenName P17 FINISHED
Object Sylvia
Sylvia is a feminine given name of Latin origin meaning "from the forest" or "of the woods."
 E30938 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: Sylvia | Statement: [Sylvia Nasar, givenName, Sylvia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sylvia
Context triple: [Sylvia Nasar, givenName, Sylvia]
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Tessie
    Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
  • C. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • D. Sophia
    Sophia of the Palatinate was a 17th-century German princess and Electress of Hanover, best known as the mother of King George I of Great Britain and a key figure in the Protestant succession to the British throne.
  • E. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • 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: Sylvia
Triple: [Sylvia Nasar, givenName, Sylvia]
Generated description
Sylvia is a feminine given name of Latin origin meaning "from the forest" or "of the woods."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sylvia
Target entity description: Sylvia is a feminine given name of Latin origin meaning "from the forest" or "of the woods."
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Tessie
    Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
  • C. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • D. Sophia
    Sophia of the Palatinate was a 17th-century German princess and Electress of Hanover, best known as the mother of King George I of Great Britain and a key figure in the Protestant succession to the British throne.
  • E. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • 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_69a25374990081909766d30c79a18e0e completed Feb. 28, 2026, 2:31 a.m.
NER Named-entity recognition batch_69a25900709c8190a65e778936be5dd5 completed Feb. 28, 2026, 2:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69a36958f6488190872ccf3c318a3cfa completed Feb. 28, 2026, 10:16 p.m.
NEDg Description generation batch_69a369de04048190b2dc01cc328e644d completed Feb. 28, 2026, 10:19 p.m.
NED2 Entity disambiguation (via description) batch_69a36a48d5a88190a727fec1c25a1d5b completed Feb. 28, 2026, 10:20 p.m.
Created at: Feb. 28, 2026, 2:39 a.m.