Triple
T16378398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heartstone |
E397735
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object |
Tamasin Barak
Tamasin Barak is a key character in the fantasy novel "Heartstone," set in a world inspired by Jane Austen with dragons and magical warfare.
|
E1211004
|
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: Tamasin Barak | Statement: [Heartstone, featuresCharacter, Tamasin Barak]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamasin Barak Context triple: [Heartstone, featuresCharacter, Tamasin Barak]
-
A.
Elika Barak
Elika Barak is the wife of former President of the Supreme Court of Israel Aharon Barak and a member of a prominent Israeli legal and academic family.
-
B.
Rutanya Alda
Rutanya Alda is a Latvian-American character actress known for her roles in films such as "The Deer Hunter," "Mommie Dearest," and numerous other horror and drama movies.
-
C.
Maya Imhoof
Maya Imhoof is a film producer best known for her work on the acclaimed Swiss drama "The Boat Is Full."
-
D.
Tamsin Pickeral
Tamsin Pickeral is an art historian and author known for her books on animal-themed art and the cultural history of animals.
-
E.
Meira Shore
Meira Shore is best known as the wife of acclaimed English actor Donald Pleasence.
- 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: Tamasin Barak Triple: [Heartstone, featuresCharacter, Tamasin Barak]
Generated description
Tamasin Barak is a key character in the fantasy novel "Heartstone," set in a world inspired by Jane Austen with dragons and magical warfare.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tamasin Barak Target entity description: Tamasin Barak is a key character in the fantasy novel "Heartstone," set in a world inspired by Jane Austen with dragons and magical warfare.
-
A.
Elika Barak
Elika Barak is the wife of former President of the Supreme Court of Israel Aharon Barak and a member of a prominent Israeli legal and academic family.
-
B.
Rutanya Alda
Rutanya Alda is a Latvian-American character actress known for her roles in films such as "The Deer Hunter," "Mommie Dearest," and numerous other horror and drama movies.
-
C.
Maya Imhoof
Maya Imhoof is a film producer best known for her work on the acclaimed Swiss drama "The Boat Is Full."
-
D.
Tamsin Pickeral
Tamsin Pickeral is an art historian and author known for her books on animal-themed art and the cultural history of animals.
-
E.
Meira Shore
Meira Shore is best known as the wife of acclaimed English actor Donald Pleasence.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e319d97e00819094aa094f52a5a93e |
completed | April 18, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00356658e881908131a3c60ed5499d |
completed | May 10, 2026, 7:36 a.m. |
| NEDg | Description generation | batch_6a00383a7180819092ea605aa8ef1672 |
completed | May 10, 2026, 7:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00391645ac819092a06dc6813604fa |
completed | May 10, 2026, 7:51 a.m. |
Created at: April 10, 2026, 5:08 a.m.