Triple
T15649135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gillett |
E376258
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Leslie Gillett
Leslie Gillett is an individual notable enough to be recognized as a prominent bearer of the surname Gillett.
|
E1169635
|
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: Leslie Gillett | Statement: [Gillett, hasNotableBearer, Leslie Gillett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leslie Gillett Context triple: [Gillett, hasNotableBearer, Leslie Gillett]
-
A.
Leslie Sharp
Leslie Sharp is a British social anthropologist known for her influential work on medical anthropology, organ transplantation, and the cultural politics of the body.
-
B.
Leslie Milne
Leslie Milne is a notable individual recognized for bearing the surname Milne.
-
C.
Leslie Hodgson
Leslie Hodgson is a film editor best known for his work on the 1985 fantasy film "Return to Oz."
-
D.
Leslie Jennings
Leslie Jennings is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Jennings.
-
E.
Leslie Crowther
Leslie Crowther was a British television comedian and game show host best known for his energetic presenting style on popular UK entertainment programs from the 1960s through the 1980s.
- 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: Leslie Gillett Triple: [Gillett, hasNotableBearer, Leslie Gillett]
Generated description
Leslie Gillett is an individual notable enough to be recognized as a prominent bearer of the surname Gillett.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leslie Gillett Target entity description: Leslie Gillett is an individual notable enough to be recognized as a prominent bearer of the surname Gillett.
-
A.
Leslie Sharp
Leslie Sharp is a British social anthropologist known for her influential work on medical anthropology, organ transplantation, and the cultural politics of the body.
-
B.
Leslie Milne
Leslie Milne is a notable individual recognized for bearing the surname Milne.
-
C.
Leslie Hodgson
Leslie Hodgson is a film editor best known for his work on the 1985 fantasy film "Return to Oz."
-
D.
Leslie Jennings
Leslie Jennings is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Jennings.
-
E.
Leslie Crowther
Leslie Crowther was a British television comedian and game show host best known for his energetic presenting style on popular UK entertainment programs from the 1960s through the 1980s.
- 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ed7212c8190be6ff76afa25f7ca |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff67957ebc8190b187f557bd01d58d |
completed | May 9, 2026, 4:57 p.m. |
| NEDg | Description generation | batch_69ff68af38848190975e374b2c8c917b |
completed | May 9, 2026, 5:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff69660b6c819082dbdf06db1c8fe3 |
completed | May 9, 2026, 5:05 p.m. |
Created at: April 10, 2026, 4:15 a.m.