Triple
T5590674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tammy and the Bachelor |
E146868
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Mala Powers
Mala Powers was an American film and television actress best known for her roles in 1950s Hollywood dramas and comedies.
|
E529980
|
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: Mala Powers | Statement: [Tammy and the Bachelor, starring, Mala Powers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mala Powers Context triple: [Tammy and the Bachelor, starring, Mala Powers]
-
A.
Donna Powers
Donna Powers is an American screenwriter best known for co-writing the 2003 heist film "The Italian Job" and other genre movies.
-
B.
Aileen Marlowe
Aileen Marlowe was the wife of American film and television actor Hugh Marlowe.
-
C.
Renee Raddick
Renee Raddick is a confident, outspoken district attorney and close friend of Ally on the television series "Ally McBeal."
-
D.
Jinx Godfrey
Jinx Godfrey is a British film editor best known for her work on acclaimed documentaries and feature films, including the Oscar-winning "Man on Wire."
-
E.
Lucia Chase
Lucia Chase was an influential American dancer, actress, and arts patron best known for co-founding and long directing the American Ballet Theatre, helping to establish it as a leading classical ballet company.
- 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: Mala Powers Triple: [Tammy and the Bachelor, starring, Mala Powers]
Generated description
Mala Powers was an American film and television actress best known for her roles in 1950s Hollywood dramas and comedies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mala Powers Target entity description: Mala Powers was an American film and television actress best known for her roles in 1950s Hollywood dramas and comedies.
-
A.
Donna Powers
Donna Powers is an American screenwriter best known for co-writing the 2003 heist film "The Italian Job" and other genre movies.
-
B.
Aileen Marlowe
Aileen Marlowe was the wife of American film and television actor Hugh Marlowe.
-
C.
Renee Raddick
Renee Raddick is a confident, outspoken district attorney and close friend of Ally on the television series "Ally McBeal."
-
D.
Jinx Godfrey
Jinx Godfrey is a British film editor best known for her work on acclaimed documentaries and feature films, including the Oscar-winning "Man on Wire."
-
E.
Lucia Chase
Lucia Chase was an influential American dancer, actress, and arts patron best known for co-founding and long directing the American Ballet Theatre, helping to establish it as a leading classical ballet company.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020a1d4cc8190a52264dfba6aa011 |
completed | March 22, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0286852148190ad4975fe746d7001 |
completed | March 22, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_69c03cd1650c8190b4673c479e151cf3 |
completed | March 22, 2026, 7:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c03d6839dc8190ae05e661c4844211 |
completed | March 22, 2026, 7:05 p.m. |
Created at: March 22, 2026, 3:38 p.m.