Triple

T16526007
Position Surface form Disambiguated ID Type / Status
Subject Dexter E401438 entity
Predicate hasNotableBearer P458 FINISHED
Object Walter Dexter
Walter Dexter is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Dexter.
 E1225750 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: Walter Dexter | Statement: [Dexter, hasNotableBearer, Walter Dexter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Walter Dexter
Context triple: [Dexter, hasNotableBearer, Walter Dexter]
  • A. Walter Eckland
    Walter Eckland is the gruff, reluctant hero and boozy beachcomber-turned-lookout portrayed by Cary Grant in the World War II comedy film "Father Goose."
  • B. Walter Frye
    Walter Frye was a 15th-century English composer known for his influential sacred vocal music, particularly masses and motets, during the early Renaissance.
  • C. Walter Brewster
    Walter Brewster was a prominent local landowner and early settler after whom the Village of Brewster in New York was named.
  • D. Walter Nelson
    Walter Nelson was an attorney who served on the defense team in the landmark Ossian Sweet murder trial, which challenged racial injustice in 1920s Detroit.
  • E. Walter March
    Walter March was a German architect best known for designing Berlin’s Olympiastadion, the main venue of the 1936 Olympic Games.
  • 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: Walter Dexter
Triple: [Dexter, hasNotableBearer, Walter Dexter]
Generated description
Walter Dexter is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Dexter.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Walter Dexter
Target entity description: Walter Dexter is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Dexter.
  • A. Walter Eckland
    Walter Eckland is the gruff, reluctant hero and boozy beachcomber-turned-lookout portrayed by Cary Grant in the World War II comedy film "Father Goose."
  • B. Walter Frye
    Walter Frye was a 15th-century English composer known for his influential sacred vocal music, particularly masses and motets, during the early Renaissance.
  • C. Walter Brewster
    Walter Brewster was a prominent local landowner and early settler after whom the Village of Brewster in New York was named.
  • D. Walter Nelson
    Walter Nelson was an attorney who served on the defense team in the landmark Ossian Sweet murder trial, which challenged racial injustice in 1920s Detroit.
  • E. Walter March
    Walter March was a German architect best known for designing Berlin’s Olympiastadion, the main venue of the 1936 Olympic Games.
  • 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_69d883838abc8190bc79cb2d41733ce2 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e32ed3f0388190a9f03473c37dfc46 completed April 18, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0084ac013c81909ce7055de4f12e58 completed May 10, 2026, 1:14 p.m.
NEDg Description generation batch_6a008558218c8190b0e72356ae52afd2 completed May 10, 2026, 1:17 p.m.
NED2 Entity disambiguation (via description) batch_6a008624706881909e9a265a37eeb3fc completed May 10, 2026, 1:20 p.m.
Created at: April 10, 2026, 5:14 a.m.