Triple
T12436709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | For Those in Peril |
E297161
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Mary Burke
Mary Burke is a film producer known for her work on independent and critically acclaimed British films.
|
E1008978
|
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: Mary Burke | Statement: [For Those in Peril, producer, Mary Burke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Burke Context triple: [For Those in Peril, producer, Mary Burke]
-
A.
Marie Burke
Marie Burke was a British actress and singer active in the early to mid-20th century, known for her work on stage, film, and radio.
-
B.
Mary Cleary
Mary Cleary was the wife of Commodore John Barry, an early U.S. naval officer often called the "Father of the American Navy."
-
C.
Mary Fitzgerald
Mary Fitzgerald is the daughter of Mary Josephine Hannon Fitzgerald, placing her in the prominent Fitzgerald family associated with early 20th-century Boston politics.
-
D.
Mary Fitzgerald
Mary Fitzgerald is a television producer known for her work on projects led by executive producer Dr. Ken.
-
E.
Mary O'Leary
Mary O'Leary is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the O'Leary surname.
- 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: Mary Burke Triple: [For Those in Peril, producer, Mary Burke]
Generated description
Mary Burke is a film producer known for her work on independent and critically acclaimed British films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Burke Target entity description: Mary Burke is a film producer known for her work on independent and critically acclaimed British films.
-
A.
Marie Burke
Marie Burke was a British actress and singer active in the early to mid-20th century, known for her work on stage, film, and radio.
-
B.
Mary Cleary
Mary Cleary was the wife of Commodore John Barry, an early U.S. naval officer often called the "Father of the American Navy."
-
C.
Mary Fitzgerald
Mary Fitzgerald is the daughter of Mary Josephine Hannon Fitzgerald, placing her in the prominent Fitzgerald family associated with early 20th-century Boston politics.
-
D.
Mary Fitzgerald
Mary Fitzgerald is a television producer known for her work on projects led by executive producer Dr. Ken.
-
E.
Mary O'Leary
Mary O'Leary is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the O'Leary surname.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d8c8fd481909b35ac504127a1b6 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a535943081909f893b2be006cc28 |
completed | May 3, 2026, 1:30 a.m. |
| NEDg | Description generation | batch_69f6a724e414819081c95b0d4ac0da25 |
completed | May 3, 2026, 1:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a7def4bc8190836ad781a4a28456 |
completed | May 3, 2026, 1:41 a.m. |
Created at: April 8, 2026, 9:55 p.m.