Triple
T5908391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noah Jupe |
E131397
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object |
Jemma Jupe
Jemma Jupe is a member of the Jupe family, known publicly as a relative of English actor Noah Jupe.
|
E552966
|
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: Jemma Jupe | Statement: [Noah Jupe, hasRelative, Jemma Jupe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jemma Jupe Context triple: [Noah Jupe, hasRelative, Jemma Jupe]
-
A.
Gemma Jones
Gemma Jones is an English actress known for her work in film, television, and theatre, including prominent roles in period dramas and popular franchises like the Bridget Jones series and the Harry Potter films.
-
B.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
C.
Tamsin
Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
-
D.
Jasmine Tookes
Jasmine Tookes is an American fashion model best known for her high-profile work with Victoria’s Secret, including serving as one of its prominent Angels.
-
E.
Gemma Flynn
Gemma Flynn is a New Zealand former international field hockey player who represented the Black Sticks at multiple Olympic Games.
- 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: Jemma Jupe Triple: [Noah Jupe, hasRelative, Jemma Jupe]
Generated description
Jemma Jupe is a member of the Jupe family, known publicly as a relative of English actor Noah Jupe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jemma Jupe Target entity description: Jemma Jupe is a member of the Jupe family, known publicly as a relative of English actor Noah Jupe.
-
A.
Gemma Jones
Gemma Jones is an English actress known for her work in film, television, and theatre, including prominent roles in period dramas and popular franchises like the Bridget Jones series and the Harry Potter films.
-
B.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
C.
Tamsin
Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
-
D.
Jasmine Tookes
Jasmine Tookes is an American fashion model best known for her high-profile work with Victoria’s Secret, including serving as one of its prominent Angels.
-
E.
Gemma Flynn
Gemma Flynn is a New Zealand former international field hockey player who represented the Black Sticks at multiple Olympic Games.
- 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03773f4188190a11276b2d5baad08 |
completed | March 22, 2026, 6:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b16f568081908839cd2403b7534c |
completed | March 23, 2026, 3:20 a.m. |
| NEDg | Description generation | batch_69c0b22e4ec88190ac3794edcd1c0b26 |
completed | March 23, 2026, 3:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0b29fbec8819092b117bd40e3731f |
completed | March 23, 2026, 3:25 a.m. |
Created at: March 22, 2026, 3:59 p.m.