Triple

T1785863
Position Surface form Disambiguated ID Type / Status
Subject Mills E39389 entity
Predicate hasNotableBearer P458 FINISHED
Object Wendy Mills
Wendy Mills is a person notable enough to be specifically cited as a bearer of the surname Mills.
 E227553 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: Wendy Mills | Statement: [Mills, hasNotableBearer, Wendy Mills]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wendy Mills
Context triple: [Mills, hasNotableBearer, Wendy Mills]
  • A. Wendy Cheesman
    Wendy Cheesman was a British architect and the first wife and early professional collaborator of renowned architect Norman Foster.
  • B. Wendy Benchley
    Wendy Benchley is an American ocean conservationist, environmental activist, and former political figure known for her leadership in marine protection and shark conservation.
  • C. Wendy Hughes
    Wendy Hughes was an acclaimed Australian actress known for her versatile performances in film, television, and theatre from the 1970s onward.
  • D. Julie Gillis
    Julie Gillis is the charming, commitment-wary nightclub agent at the center of the 1955 romantic comedy film "The Tender Trap," whose bachelor lifestyle is upended by unexpected love.
  • E. Wendy Lawrence
    Wendy Lawrence is a retired U.S. Navy captain and NASA astronaut who flew on multiple Space Shuttle missions as a mission specialist.
  • 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: Wendy Mills
Triple: [Mills, hasNotableBearer, Wendy Mills]
Generated description
Wendy Mills is a person notable enough to be specifically cited as a bearer of the surname Mills.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wendy Mills
Target entity description: Wendy Mills is a person notable enough to be specifically cited as a bearer of the surname Mills.
  • A. Wendy Cheesman
    Wendy Cheesman was a British architect and the first wife and early professional collaborator of renowned architect Norman Foster.
  • B. Wendy Benchley
    Wendy Benchley is an American ocean conservationist, environmental activist, and former political figure known for her leadership in marine protection and shark conservation.
  • C. Wendy Hughes
    Wendy Hughes was an acclaimed Australian actress known for her versatile performances in film, television, and theatre from the 1970s onward.
  • D. Julie Gillis
    Julie Gillis is the charming, commitment-wary nightclub agent at the center of the 1955 romantic comedy film "The Tender Trap," whose bachelor lifestyle is upended by unexpected love.
  • E. Wendy Lawrence
    Wendy Lawrence is a retired U.S. Navy captain and NASA astronaut who flew on multiple Space Shuttle missions as a mission specialist.
  • 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_69a88630519c8190a17addd83c4a3ef4 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa650d304481908ad9bff3eadf7da6 completed March 6, 2026, 5:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1fc41d248190a149252940dbdb27 completed March 9, 2026, 1:17 a.m.
NEDg Description generation batch_69ae204fe6148190915219beb27128bc completed March 9, 2026, 1:20 a.m.
NED2 Entity disambiguation (via description) batch_69ae20d09c748190aebbfb88f0eedbaa completed March 9, 2026, 1:22 a.m.
Created at: March 4, 2026, 7:31 p.m.