Triple
T4295754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Leys School |
E99706
|
entity |
| Predicate | hasAlumnus |
P51
|
FINISHED |
| Object |
Andy Bond
Andy Bond is a British businessman best known for serving as the chief executive of Asda, one of the UK’s largest supermarket chains.
|
E428445
|
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: Andy Bond | Statement: [The Leys School, hasAlumnus, Andy Bond]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andy Bond Context triple: [The Leys School, hasAlumnus, Andy Bond]
-
A.
Brian Bondy
Brian Bondy is a software engineer and entrepreneur best known as a co-founder and former CTO of the privacy-focused Brave web browser.
-
B.
Alan Bond
Alan Bond was a controversial Australian businessman and entrepreneur best known for his role in winning the 1983 America’s Cup and for his later corporate scandals and imprisonment.
-
C.
Dan Bunting
Dan Bunting is a video game developer best known for his leadership and design work on the Call of Duty series at Treyarch.
-
D.
Michael Bradsell
Michael Bradsell is a British science fiction editor best known for his work on the magazine Jabberwocky.
-
E.
Michael Bradsell
Michael Bradsell is a British film editor known for his work on notable films including Kenneth Branagh’s adaptation of "Henry V" (1989).
- 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: Andy Bond Triple: [The Leys School, hasAlumnus, Andy Bond]
Generated description
Andy Bond is a British businessman best known for serving as the chief executive of Asda, one of the UK’s largest supermarket chains.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Andy Bond Target entity description: Andy Bond is a British businessman best known for serving as the chief executive of Asda, one of the UK’s largest supermarket chains.
-
A.
Brian Bondy
Brian Bondy is a software engineer and entrepreneur best known as a co-founder and former CTO of the privacy-focused Brave web browser.
-
B.
Alan Bond
Alan Bond was a controversial Australian businessman and entrepreneur best known for his role in winning the 1983 America’s Cup and for his later corporate scandals and imprisonment.
-
C.
Dan Bunting
Dan Bunting is a video game developer best known for his leadership and design work on the Call of Duty series at Treyarch.
-
D.
Michael Bradsell
Michael Bradsell is a British science fiction editor best known for his work on the magazine Jabberwocky.
-
E.
Michael Bradsell
Michael Bradsell is a British film editor known for his work on notable films including Kenneth Branagh’s adaptation of "Henry V" (1989).
- 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_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35085b864819086cf726285384566 |
completed | March 12, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c740c4a081909a63fb957f2926ae |
completed | March 14, 2026, 8:38 p.m. |
| NEDg | Description generation | batch_69b5c7d04508819087b14c5c86f1e015 |
completed | March 14, 2026, 8:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5c84ccea08190a8e7e8fa93934ea2 |
completed | March 14, 2026, 8:42 p.m. |
Created at: March 12, 2026, 11:08 p.m.