Triple

T1016271
Position Surface form Disambiguated ID Type / Status
Subject Verna Fields E21937 entity
Predicate givenName P17 FINISHED
Object Verna
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
 E123170 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: Verna | Statement: [Verna Fields, givenName, Verna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Verna
Context triple: [Verna Fields, givenName, Verna]
  • A. Wilella
    Wilella is the full given name of American novelist Willa Cather, renowned for her works depicting frontier life on the Great Plains.
  • B. Loralai
    Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
  • C. Tahlequah
    Tahlequah is a city in eastern Oklahoma that serves as the capital of the Cherokee Nation and is known for its rich Native American history and culture.
  • D. Winona
    Winona is a historic river city in southeastern Minnesota known for its Mississippi River bluffs, cultural festivals, and regional educational institutions.
  • E. Wauchope
    Wauchope is a rural town in New South Wales, Australia, known as a gateway to the hinterland near Port Macquarie and the surrounding Mid North Coast region.
  • 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: Verna
Triple: [Verna Fields, givenName, Verna]
Generated description
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Verna
Target entity description: Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
  • A. Wilella
    Wilella is the full given name of American novelist Willa Cather, renowned for her works depicting frontier life on the Great Plains.
  • B. Loralai
    Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
  • C. Tahlequah
    Tahlequah is a city in eastern Oklahoma that serves as the capital of the Cherokee Nation and is known for its rich Native American history and culture.
  • D. Winona
    Winona is a historic river city in southeastern Minnesota known for its Mississippi River bluffs, cultural festivals, and regional educational institutions.
  • E. Wauchope
    Wauchope is a rural town in New South Wales, Australia, known as a gateway to the hinterland near Port Macquarie and the surrounding Mid North Coast region.
  • 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_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7c1e9d08190baf7e81f3777168d completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac4292482c81909bf39cd0bb288599 completed March 7, 2026, 3:21 p.m.
NEDg Description generation batch_69ac4340c7f88190a5fe3dc830bb6df0 completed March 7, 2026, 3:24 p.m.
NED2 Entity disambiguation (via description) batch_69ac43b393748190a5fa81b7ab7fa911 completed March 7, 2026, 3:26 p.m.
Created at: March 1, 2026, 7:41 p.m.