Triple
T93126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geoffrey Hinton |
E1872
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Geoffrey
Geoffrey is a masculine given name of English origin, famously borne by pioneering computer scientist and AI researcher Geoffrey Hinton.
|
E28362
|
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: Geoffrey | Statement: [Geoffrey Hinton, givenName, Geoffrey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geoffrey Context triple: [Geoffrey Hinton, givenName, Geoffrey]
-
A.
Guillaume
Guillaume is the French form of the given name William, commonly used in French-speaking countries.
-
B.
Bertram
Bertram is a masculine given name of Germanic origin, historically associated with nobility and later borne by various notable figures in arts, architecture, and literature.
-
C.
George
George is the first name of George Washington, the first President of the United States and a key leader in the American Revolutionary War.
-
D.
John Alexander
John Alexander was a prominent landowner in colonial Virginia whose family holdings encompassed the area that later became the city of Alexandria.
-
E.
Hugh
Hugh is a masculine given name of Germanic origin, commonly used in English-speaking countries.
- 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: Geoffrey Triple: [Geoffrey Hinton, givenName, Geoffrey]
Generated description
Geoffrey is a masculine given name of English origin, famously borne by pioneering computer scientist and AI researcher Geoffrey Hinton.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Geoffrey Target entity description: Geoffrey is a masculine given name of English origin, famously borne by pioneering computer scientist and AI researcher Geoffrey Hinton.
-
A.
Guillaume
Guillaume is the French form of the given name William, commonly used in French-speaking countries.
-
B.
Bertram
Bertram is a masculine given name of Germanic origin, historically associated with nobility and later borne by various notable figures in arts, architecture, and literature.
-
C.
George
George is the first name of George Washington, the first President of the United States and a key leader in the American Revolutionary War.
-
D.
John Alexander
John Alexander was a prominent landowner in colonial Virginia whose family holdings encompassed the area that later became the city of Alexandria.
-
E.
Hugh
Hugh is a masculine given name of Germanic origin, commonly used in English-speaking countries.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24fd28e988190bde699647ee5b16b |
completed | Feb. 28, 2026, 2:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a34d8edb8c81909c7229fe6e4c0569 |
completed | Feb. 28, 2026, 8:18 p.m. |
| NEDg | Description generation | batch_69a34dfe27a081909498374e791fb725 |
completed | Feb. 28, 2026, 8:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a34ed268f88190a4a4c53bc52a7182 |
completed | Feb. 28, 2026, 8:23 p.m. |
Created at: Feb. 28, 2026, 2:07 a.m.