Triple
T16097568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Insider |
E390525
|
entity |
| Predicate | presenter |
P83
|
FINISHED |
| Object |
Anya Sarre
Anya Sarre is a fashion and style expert best known for her on-air work as a stylist and commentator on the entertainment news program "The Insider."
|
E1209702
|
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: Anya Sarre | Statement: [The Insider, presenter, Anya Sarre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anya Sarre Context triple: [The Insider, presenter, Anya Sarre]
-
A.
Alisa Freindlich
Alisa Freindlich is a renowned Soviet and Russian actress celebrated for her work in film and theater, particularly in the late 20th century.
-
B.
Sofia Rosinsky
Sofia Rosinsky is an American actress best known for her starring role in the science fiction television series "Paper Girls."
-
C.
Sonia Sorel
Sonia Sorel was an American actress best known for her work in mid-20th-century film and television and as part of a prominent Hollywood family.
-
D.
Anny Danché
Anny Danché is a human editor, likely involved in preparing, revising, or curating written or published materials.
-
E.
Tatiana Behrs
Tatiana Behrs was a 19th-century Russian noblewoman best known as the sister of Sofya Andreyevna Tolstaya, the wife of writer Leo Tolstoy.
- 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: Anya Sarre Triple: [The Insider, presenter, Anya Sarre]
Generated description
Anya Sarre is a fashion and style expert best known for her on-air work as a stylist and commentator on the entertainment news program "The Insider."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anya Sarre Target entity description: Anya Sarre is a fashion and style expert best known for her on-air work as a stylist and commentator on the entertainment news program "The Insider."
-
A.
Alisa Freindlich
Alisa Freindlich is a renowned Soviet and Russian actress celebrated for her work in film and theater, particularly in the late 20th century.
-
B.
Sofia Rosinsky
Sofia Rosinsky is an American actress best known for her starring role in the science fiction television series "Paper Girls."
-
C.
Sonia Sorel
Sonia Sorel was an American actress best known for her work in mid-20th-century film and television and as part of a prominent Hollywood family.
-
D.
Anny Danché
Anny Danché is a human editor, likely involved in preparing, revising, or curating written or published materials.
-
E.
Tatiana Behrs
Tatiana Behrs was a 19th-century Russian noblewoman best known as the sister of Sofya Andreyevna Tolstaya, the wife of writer Leo Tolstoy.
- 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff6551a48190afb7e0c61e22b541 |
completed | April 17, 2026, 9:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003546d3e081908f1244b7f4fb1067 |
completed | May 10, 2026, 7:35 a.m. |
| NEDg | Description generation | batch_6a0035cfc31c8190a8ab73bbc1aacaca |
completed | May 10, 2026, 7:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00369714a88190a5e4733b67fdacfb |
completed | May 10, 2026, 7:41 a.m. |
Created at: April 10, 2026, 4:59 a.m.