Triple
T5959464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shirley MacLaine |
E132597
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Steve Parker
Steve Parker was an American film producer and manager best known for his long marriage to actress Shirley MacLaine and his work on international film projects.
|
E558096
|
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: Steve Parker | Statement: [Shirley MacLaine, spouse, Steve Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steve Parker Context triple: [Shirley MacLaine, spouse, Steve Parker]
-
A.
Michael Parker
Michael Parker is a film editor best known for his work on the British comedy-drama "Made in Dagenham."
-
B.
Steve Richards
Steve Richards is a film producer known for his work on action and genre movies, including the 2010 adaptation of "The Losers."
-
C.
Shaun Parkes
Shaun Parkes is a British actor known for his versatile performances in film, television, and theatre, including prominent roles in series such as "Casanova," "Doctor Who," and "Small Axe."
-
D.
Stephen Peters
Stephen Peters is an American screenwriter best known for writing the neo-noir thriller film "Wild Things."
-
E.
Keith Peters
Keith Peters is a prominent British physician and medical researcher known for his leadership in academic medicine and contributions to immunology and medical science policy.
- 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: Steve Parker Triple: [Shirley MacLaine, spouse, Steve Parker]
Generated description
Steve Parker was an American film producer and manager best known for his long marriage to actress Shirley MacLaine and his work on international film projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steve Parker Target entity description: Steve Parker was an American film producer and manager best known for his long marriage to actress Shirley MacLaine and his work on international film projects.
-
A.
Michael Parker
Michael Parker is a film editor best known for his work on the British comedy-drama "Made in Dagenham."
-
B.
Steve Richards
Steve Richards is a film producer known for his work on action and genre movies, including the 2010 adaptation of "The Losers."
-
C.
Shaun Parkes
Shaun Parkes is a British actor known for his versatile performances in film, television, and theatre, including prominent roles in series such as "Casanova," "Doctor Who," and "Small Axe."
-
D.
Stephen Peters
Stephen Peters is an American screenwriter best known for writing the neo-noir thriller film "Wild Things."
-
E.
Keith Peters
Keith Peters is a prominent British physician and medical researcher known for his leadership in academic medicine and contributions to immunology and medical science policy.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c039fbf49881909d97b4abb3c5286d |
completed | March 22, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e3e8f234819099336503a797e55b |
completed | March 23, 2026, 6:55 a.m. |
| NEDg | Description generation | batch_69c0ebb1dcb88190a101d3c88c647b41 |
completed | March 23, 2026, 7:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0ec4d1da081909cc6320078db4e53 |
completed | March 23, 2026, 7:31 a.m. |
Created at: March 22, 2026, 4:02 p.m.