Triple
T16866419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sean Daniel |
E410049
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Elizabeth Rapoport
Elizabeth Rapoport is a television writer and producer known for her work on various American TV series.
|
E1264503
|
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: Elizabeth Rapoport | Statement: [Sean Daniel, spouse, Elizabeth Rapoport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Rapoport Context triple: [Sean Daniel, spouse, Elizabeth Rapoport]
-
A.
Helene Shapiro
Helene Shapiro is an American mathematician known for her work in linear algebra and matrix theory, and as a student of Olga Taussky-Todd.
-
B.
Mila Pfefferberg
Mila Pfefferberg was a Holocaust survivor known for her and her husband Leopold Page’s role in preserving and sharing the story of Oskar Schindler and the Jews he saved.
-
C.
Ruth Weinstein
Ruth Weinstein is one of the children of disgraced American film producer Harvey Weinstein.
-
D.
Miriam Fried
Miriam Fried is an acclaimed Israeli-American violinist renowned for her solo performances, chamber music collaborations, and influential teaching career.
-
E.
Miriam Mendelsohn
Miriam Mendelsohn is a loyal, upbeat, and supportive best friend of Mei Lee in Pixar's animated film "Turning Red."
- 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: Elizabeth Rapoport Triple: [Sean Daniel, spouse, Elizabeth Rapoport]
Generated description
Elizabeth Rapoport is a television writer and producer known for her work on various American TV series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Rapoport Target entity description: Elizabeth Rapoport is a television writer and producer known for her work on various American TV series.
-
A.
Helene Shapiro
Helene Shapiro is an American mathematician known for her work in linear algebra and matrix theory, and as a student of Olga Taussky-Todd.
-
B.
Mila Pfefferberg
Mila Pfefferberg was a Holocaust survivor known for her and her husband Leopold Page’s role in preserving and sharing the story of Oskar Schindler and the Jews he saved.
-
C.
Ruth Weinstein
Ruth Weinstein is one of the children of disgraced American film producer Harvey Weinstein.
-
D.
Miriam Fried
Miriam Fried is an acclaimed Israeli-American violinist renowned for her solo performances, chamber music collaborations, and influential teaching career.
-
E.
Miriam Mendelsohn
Miriam Mendelsohn is a loyal, upbeat, and supportive best friend of Mei Lee in Pixar's animated film "Turning Red."
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b5088f208190abfe937633ebe3fe |
completed | April 18, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01954069e0819087fab0a782a83f39 |
completed | May 11, 2026, 8:37 a.m. |
| NEDg | Description generation | batch_6a019b79d7148190abf4b41f0c84c62e |
completed | May 11, 2026, 9:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a019c1b60f08190a6469602751e3471 |
completed | May 11, 2026, 9:06 a.m. |
Created at: April 10, 2026, 5:24 a.m.