Triple

T10263692
Position Surface form Disambiguated ID Type / Status
Subject Wiegand E240662 entity
Predicate hasNotableBearer P458 FINISHED
Object Patrick Wiegand
Patrick Wiegand is a British cartographer and academic known for his work on map design and geography education.
E906171 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: Patrick Wiegand | Statement: [Wiegand, hasNotableBearer, Patrick Wiegand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Patrick Wiegand
Context triple: [Wiegand, hasNotableBearer, Patrick Wiegand]
  • A. Thomas Wiegand
    Thomas Wiegand is a German electrical engineer and video coding expert best known for his leading role in developing the H.264/AVC video compression standard.
  • B. Richard Riehle
    Richard Riehle is an American character actor known for his prolific work in film and television, including memorable roles in movies like "Office Space" and numerous guest appearances on popular TV series.
  • C. Kirk Stievely
    Kirk Stievely is a British actor best known as the former husband of actress Victoria Tennant.
  • D. Wayne Wahrman
    Wayne Wahrman is a film editor best known for his work on major Hollywood productions, including the post-apocalyptic thriller "I Am Legend."
  • E. John Diehl
    John Diehl is an American character actor best known for his role as Detective Larry Zito on the 1980s television series "Miami Vice."
  • 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: Patrick Wiegand
Triple: [Wiegand, hasNotableBearer, Patrick Wiegand]
Generated description
Patrick Wiegand is a British cartographer and academic known for his work on map design and geography education.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Patrick Wiegand
Target entity description: Patrick Wiegand is a British cartographer and academic known for his work on map design and geography education.
  • A. Thomas Wiegand
    Thomas Wiegand is a German electrical engineer and video coding expert best known for his leading role in developing the H.264/AVC video compression standard.
  • B. Richard Riehle
    Richard Riehle is an American character actor known for his prolific work in film and television, including memorable roles in movies like "Office Space" and numerous guest appearances on popular TV series.
  • C. Kirk Stievely
    Kirk Stievely is a British actor best known as the former husband of actress Victoria Tennant.
  • D. Wayne Wahrman
    Wayne Wahrman is a film editor best known for his work on major Hollywood productions, including the post-apocalyptic thriller "I Am Legend."
  • E. John Diehl
    John Diehl is an American character actor best known for his role as Detective Larry Zito on the 1980s television series "Miami Vice."
  • 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_69d381a7e198819090280d5ab885d59e completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d25e68fc8190b46699d2266c0505 completed April 7, 2026, 9:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69e42d406e2c8190ad27f3a5276be25f completed April 19, 2026, 1:17 a.m.
NEDg Description generation batch_69e4374700b881908ebb185ae020487b completed April 19, 2026, 2 a.m.
NED2 Entity disambiguation (via description) batch_69e4399385c08190852c3cbd730a1f11 completed April 19, 2026, 2:10 a.m.
Created at: April 6, 2026, 11:33 a.m.