Triple
T20347744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | And Now… Ladies and Gentlemen |
E495916
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Tania Garbarski
Tania Garbarski is a Belgian actress known for her work in film, television, and theater, often appearing in French-language productions.
|
E1431023
|
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: Tania Garbarski | Statement: [And Now… Ladies and Gentlemen, castMember, Tania Garbarski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tania Garbarski Context triple: [And Now… Ladies and Gentlemen, castMember, Tania Garbarski]
-
A.
Tania Kosevich
Tania Kosevich is best known as the wife of British comedian, actor, and Monty Python member Eric Idle.
-
B.
Maria Likarz
Maria Likarz was an Austrian designer and artist known for her influential work in textiles, fashion, and graphic design in early 20th-century Vienna.
-
C.
Nina Siemaszko
Nina Siemaszko is an American film and television actress known for her supporting roles in movies like "The Haunting of Molly Hartley" and various popular TV series.
-
D.
Tania Alexander
Tania Alexander is a British television producer best known for developing and producing the popular reality series "Gogglebox."
-
E.
Tanya Biank
Tanya Biank is an American journalist and author known for her in-depth reporting and books on the lives and challenges of military families.
- 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: Tania Garbarski Triple: [And Now… Ladies and Gentlemen, castMember, Tania Garbarski]
Generated description
Tania Garbarski is a Belgian actress known for her work in film, television, and theater, often appearing in French-language productions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tania Garbarski Target entity description: Tania Garbarski is a Belgian actress known for her work in film, television, and theater, often appearing in French-language productions.
-
A.
Tania Kosevich
Tania Kosevich is best known as the wife of British comedian, actor, and Monty Python member Eric Idle.
-
B.
Maria Likarz
Maria Likarz was an Austrian designer and artist known for her influential work in textiles, fashion, and graphic design in early 20th-century Vienna.
-
C.
Nina Siemaszko
Nina Siemaszko is an American film and television actress known for her supporting roles in movies like "The Haunting of Molly Hartley" and various popular TV series.
-
D.
Tania Alexander
Tania Alexander is a British television producer best known for developing and producing the popular reality series "Gogglebox."
-
E.
Tanya Biank
Tanya Biank is an American journalist and author known for her in-depth reporting and books on the lives and challenges of military families.
- 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67839e7b48190876ce7133a20c65b |
completed | April 20, 2026, 7:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0883e66df48190a0936733c650d87e |
completed | May 16, 2026, 2:49 p.m. |
| NEDg | Description generation | batch_6a08853634b48190b6cc16e68ed8e39e |
completed | May 16, 2026, 2:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0885bfe3588190916a15b060089de9 |
completed | May 16, 2026, 2:57 p.m. |
Created at: April 16, 2026, 11:24 a.m.