Triple
T2297657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamsin Egerton |
E51654
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Tamsin
Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
|
E253222
|
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: Tamsin | Statement: [Tamsin Egerton, givenName, Tamsin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamsin Context triple: [Tamsin Egerton, givenName, Tamsin]
-
A.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
B.
Fiona
Fiona is an American singer-songwriter and pianist known for her emotionally intense, critically acclaimed alternative music.
-
C.
Maisie Farange
Maisie Farange is the perceptive child protagonist of Henry James’s novel "What Maisie Knew," whose experiences reveal the emotional fallout of her parents’ bitter divorce.
-
D.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
E.
Catriona
Catriona is a feminine given name of Gaelic origin, commonly used in Scotland and Ireland and often considered a variant of Katherine.
- 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: Tamsin Triple: [Tamsin Egerton, givenName, Tamsin]
Generated description
Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tamsin Target entity description: Tamsin is a feminine given name of English origin, often associated with actresses and public figures such as Tamsin Egerton.
-
A.
Tessa
Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
-
B.
Fiona
Fiona is an American singer-songwriter and pianist known for her emotionally intense, critically acclaimed alternative music.
-
C.
Maisie Farange
Maisie Farange is the perceptive child protagonist of Henry James’s novel "What Maisie Knew," whose experiences reveal the emotional fallout of her parents’ bitter divorce.
-
D.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
E.
Catriona
Catriona is a feminine given name of Gaelic origin, commonly used in Scotland and Ireland and often considered a variant of Katherine.
- 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_69a88b0a9f248190bcff941463d8f65a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc5df37808190ba6a43dc1e9e723a |
completed | March 7, 2026, 6:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7f2ba4048190898e3524feb0d96d |
completed | March 9, 2026, 8:04 a.m. |
| NEDg | Description generation | batch_69ae802a066881909aa4e7b00e29306f |
completed | March 9, 2026, 8:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae80c37ff48190a24b7806320ebc00 |
completed | March 9, 2026, 8:11 a.m. |
Created at: March 4, 2026, 7:49 p.m.