Triple
T23172329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pacific Heights |
E578891
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Pamela Power
Pamela Power is a film editor known for her work on the thriller "Pacific Heights" and other motion pictures.
|
E1573098
|
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: Pamela Power | Statement: [Pacific Heights, editedBy, Pamela Power]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pamela Power Context triple: [Pacific Heights, editedBy, Pamela Power]
-
A.
Pamela Reeves
Pamela Reeves was a respected American attorney and federal judge who served on the U.S. District Court for the Eastern District of Tennessee and was known for her trailblazing role as the court’s first female chief judge.
-
B.
Pamela Hart
Pamela Hart is an actress best known for her role in the 1998 psychological thriller film "Pi."
-
C.
Pamela Marmont
Pamela Marmont was a British actress from a prominent theatrical family, known for her stage and screen work in the mid-20th century.
-
D.
Pamela Brown
Pamela Brown was a British stage and film actress known for her intense character roles in mid-20th-century cinema and theatre.
-
E.
Pamela Wynant
Pamela Wynant is a fictional character in Dashiell Hammett’s novel "The Thin Man," known as the sophisticated and troubled wife of inventor Clyde Wynant.
- 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: Pamela Power Triple: [Pacific Heights, editedBy, Pamela Power]
Generated description
Pamela Power is a film editor known for her work on the thriller "Pacific Heights" and other motion pictures.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pamela Power Target entity description: Pamela Power is a film editor known for her work on the thriller "Pacific Heights" and other motion pictures.
-
A.
Pamela Reeves
Pamela Reeves was a respected American attorney and federal judge who served on the U.S. District Court for the Eastern District of Tennessee and was known for her trailblazing role as the court’s first female chief judge.
-
B.
Pamela Hart
Pamela Hart is an actress best known for her role in the 1998 psychological thriller film "Pi."
-
C.
Pamela Marmont
Pamela Marmont was a British actress from a prominent theatrical family, known for her stage and screen work in the mid-20th century.
-
D.
Pamela Brown
Pamela Brown was a British stage and film actress known for her intense character roles in mid-20th-century cinema and theatre.
-
E.
Pamela Wynant
Pamela Wynant is a fictional character in Dashiell Hammett’s novel "The Thin Man," known as the sophisticated and troubled wife of inventor Clyde Wynant.
- 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_69e245fd2a388190b814c0dfa15f7148 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18f30ce148190a6de928c8213399e |
completed | April 29, 2026, 4:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c30aa91488190a115f3a44ff25348 |
completed | May 19, 2026, 9:43 a.m. |
| NEDg | Description generation | batch_6a0c326f3b4c819099ea8776b84fa279 |
completed | May 19, 2026, 9:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c32ece3148190a22881d69c42e948 |
completed | May 19, 2026, 9:52 a.m. |
Created at: April 17, 2026, 4:04 p.m.