Triple
T14874040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transamerica |
E349820
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Pam Wise
Pam Wise is a film editor best known for her work on the acclaimed independent drama "Transamerica."
|
E1210589
|
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: Pam Wise | Statement: [Transamerica, editedBy, Pam Wise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pam Wise Context triple: [Transamerica, editedBy, Pam Wise]
-
A.
Pamela Jenkins
Pamela Jenkins is a fictional character from the Saw horror film franchise, appearing in the movie "Saw VI."
-
B.
Pamela Frank
Pamela Frank is an acclaimed American violinist renowned for her expressive performances and influential teaching career.
-
C.
Pamela Frank
Pamela Frank is the second wife of singer and civil rights activist Harry Belafonte, known primarily for her long-term marriage to the entertainer.
-
D.
Pam Marsden
Pam Marsden is a film producer best known for her work on animated features, including serving as a producer on Disney's "Dinosaur" (2000).
-
E.
Pamela Franklin
Pamela Franklin is a British actress best known for her work as a child and young adult in 1960s and 1970s films and television, particularly in psychological horror and drama.
- 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: Pam Wise Triple: [Transamerica, editedBy, Pam Wise]
Generated description
Pam Wise is a film editor best known for her work on the acclaimed independent drama "Transamerica."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pam Wise Target entity description: Pam Wise is a film editor best known for her work on the acclaimed independent drama "Transamerica."
-
A.
Pamela Jenkins
Pamela Jenkins is a fictional character from the Saw horror film franchise, appearing in the movie "Saw VI."
-
B.
Pamela Frank
Pamela Frank is an acclaimed American violinist renowned for her expressive performances and influential teaching career.
-
C.
Pamela Frank
Pamela Frank is the second wife of singer and civil rights activist Harry Belafonte, known primarily for her long-term marriage to the entertainer.
-
D.
Pam Marsden
Pam Marsden is a film producer best known for her work on animated features, including serving as a producer on Disney's "Dinosaur" (2000).
-
E.
Pamela Franklin
Pamela Franklin is a British actress best known for her work as a child and young adult in 1960s and 1970s films and television, particularly in psychological horror and drama.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e3e5d48190a132f2cf012b01e2 |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0035428e608190b8bb41dabda044d1 |
completed | May 10, 2026, 7:35 a.m. |
| NEDg | Description generation | batch_6a00377305bc8190a566c4ed4aed70c9 |
completed | May 10, 2026, 7:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0037e43a2c8190993447ade595f6e6 |
completed | May 10, 2026, 7:46 a.m. |
Created at: April 10, 2026, 1:55 a.m.