Triple
T9384663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jono |
E225873
|
entity |
| Predicate | notableBearerExample |
P458
|
FINISHED |
| Object |
Jono Lancaster
Jono Lancaster is a British motivational speaker and advocate known for raising awareness about Treacher Collins syndrome and promoting acceptance of facial differences.
|
E798772
|
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: Jono Lancaster | Statement: [Jono, notableBearerExample, Jono Lancaster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jono Lancaster Context triple: [Jono, notableBearerExample, Jono Lancaster]
-
A.
Jono Coleman
Jono Coleman was an Australian radio and television presenter known for his long-running broadcasting career in both Australia and the United Kingdom.
-
B.
Darren Gilshenan
Darren Gilshenan is an Australian actor and comedian known for his work in television, film, and theatre, particularly in character and comic roles.
-
C.
Jonny Keeling
Jonny Keeling is a British television producer known for his work on major BBC natural history documentaries.
-
D.
Nicholas Lucas
Nicholas Lucas was an early colonial leader known for helping establish the West Jersey settlement in what is now New Jersey.
-
E.
Ian Mackley
Ian Mackley is the husband of British comedian and television personality Julian Clary.
- 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: Jono Lancaster Triple: [Jono, notableBearerExample, Jono Lancaster]
Generated description
Jono Lancaster is a British motivational speaker and advocate known for raising awareness about Treacher Collins syndrome and promoting acceptance of facial differences.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jono Lancaster Target entity description: Jono Lancaster is a British motivational speaker and advocate known for raising awareness about Treacher Collins syndrome and promoting acceptance of facial differences.
-
A.
Jono Coleman
Jono Coleman was an Australian radio and television presenter known for his long-running broadcasting career in both Australia and the United Kingdom.
-
B.
Darren Gilshenan
Darren Gilshenan is an Australian actor and comedian known for his work in television, film, and theatre, particularly in character and comic roles.
-
C.
Jonny Keeling
Jonny Keeling is a British television producer known for his work on major BBC natural history documentaries.
-
D.
Nicholas Lucas
Nicholas Lucas was an early colonial leader known for helping establish the West Jersey settlement in what is now New Jersey.
-
E.
Ian Mackley
Ian Mackley is the husband of British comedian and television personality Julian Clary.
- 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_69ca842e9dcc8190a264119e683cfe04 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd50d142008190840e131e8f1940f9 |
completed | April 1, 2026, 5:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1101e83e8819093fe8d25c819820b |
completed | April 4, 2026, 1:20 p.m. |
| NEDg | Description generation | batch_69d110d745648190a0c3b62cc34932ba |
completed | April 4, 2026, 1:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1112ef0f881909ab2ffdaea160c40 |
completed | April 4, 2026, 1:25 p.m. |
Created at: March 30, 2026, 7:44 p.m.