Triple
T23062685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyad |
E574945
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Julia Cox
Julia Cox is a screenwriter best known for writing the biographical sports drama film "Nyad."
|
E1585998
|
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: Julia Cox | Statement: [Nyad, screenwriter, Julia Cox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julia Cox Context triple: [Nyad, screenwriter, Julia Cox]
-
A.
Emily Alyn Lind
Emily Alyn Lind is an American actress known for her roles in film and television, including playing the young Amanda Clarke on the TV series "Revenge."
-
B.
Cristin Milioti
Cristin Milioti is an American actress and singer known for her work in film, television, and theater, including roles in "How I Met Your Mother," "The Wolf of Wall Street," and "Palm Springs."
-
C.
Emily Patterson
Emily Patterson is the daughter of American actress Téa Leoni.
-
D.
Annalise Basso
Annalise Basso is an American actress known for her roles in independent films and television, including her performance in the critically acclaimed drama "Captain Fantastic."
-
E.
Eliza Scanlen
Eliza Scanlen is an Australian actress known for her roles in film and television, including prominent performances in projects like "Sharp Objects" and "Little Women."
- 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: Julia Cox Triple: [Nyad, screenwriter, Julia Cox]
Generated description
Julia Cox is a screenwriter best known for writing the biographical sports drama film "Nyad."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Julia Cox Target entity description: Julia Cox is a screenwriter best known for writing the biographical sports drama film "Nyad."
-
A.
Emily Alyn Lind
Emily Alyn Lind is an American actress known for her roles in film and television, including playing the young Amanda Clarke on the TV series "Revenge."
-
B.
Cristin Milioti
Cristin Milioti is an American actress and singer known for her work in film, television, and theater, including roles in "How I Met Your Mother," "The Wolf of Wall Street," and "Palm Springs."
-
C.
Emily Patterson
Emily Patterson is the daughter of American actress Téa Leoni.
-
D.
Annalise Basso
Annalise Basso is an American actress known for her roles in independent films and television, including her performance in the critically acclaimed drama "Captain Fantastic."
-
E.
Eliza Scanlen
Eliza Scanlen is an Australian actress known for her roles in film and television, including prominent performances in projects like "Sharp Objects" and "Little Women."
- 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a0c3c881909f137ad511c216ac |
completed | April 29, 2026, 4:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c67729c508190b90b2b7c7d1444f9 |
completed | May 19, 2026, 1:36 p.m. |
| NEDg | Description generation | batch_6a0c72132a608190b44c96de39b7187a |
completed | May 19, 2026, 2:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c76e904b08190a5329a3ed56291dc |
completed | May 19, 2026, 2:42 p.m. |
Created at: April 17, 2026, 3:55 p.m.