Triple
T2232402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Superman III |
E49198
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Leslie Newman
Leslie Newman is a screenwriter best known for co-writing several of the Christopher Reeve-era Superman films, including Superman III.
|
E305386
|
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: Leslie Newman | Statement: [Superman III, screenwriter, Leslie Newman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leslie Newman Context triple: [Superman III, screenwriter, Leslie Newman]
-
A.
Leslie Easterbrook
Leslie Easterbrook is an American actress best known for her role as Sergeant Debbie Callahan in the "Police Academy" film series.
-
B.
Leslie Alexander
Leslie Alexander is known primarily as a child of American politician and former U.S. Senator Lamar Alexander.
-
C.
Leslie Fenton
Leslie Fenton was a British-born American actor and film director active in Hollywood during the early to mid-20th century.
-
D.
Katherine Rogers
Katherine Rogers was the mother of John Harvard, the English clergyman whose bequest helped found Harvard College in colonial Massachusetts.
-
E.
Rachel Marron
Rachel Marron is a famous pop singer and actress who becomes the client and love interest of a former Secret Service agent in the romantic thriller film "The Bodyguard."
- 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: Leslie Newman Triple: [Superman III, screenwriter, Leslie Newman]
Generated description
Leslie Newman is a screenwriter best known for co-writing several of the Christopher Reeve-era Superman films, including Superman III.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leslie Newman Target entity description: Leslie Newman is a screenwriter best known for co-writing several of the Christopher Reeve-era Superman films, including Superman III.
-
A.
Leslie Easterbrook
Leslie Easterbrook is an American actress best known for her role as Sergeant Debbie Callahan in the "Police Academy" film series.
-
B.
Leslie Alexander
Leslie Alexander is known primarily as a child of American politician and former U.S. Senator Lamar Alexander.
-
C.
Leslie Fenton
Leslie Fenton was a British-born American actor and film director active in Hollywood during the early to mid-20th century.
-
D.
Katherine Rogers
Katherine Rogers was the mother of John Harvard, the English clergyman whose bequest helped found Harvard College in colonial Massachusetts.
-
E.
Rachel Marron
Rachel Marron is a famous pop singer and actress who becomes the client and love interest of a former Secret Service agent in the romantic thriller film "The Bodyguard."
- 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc06d26bc8190a85ddb6312d2df08 |
completed | March 7, 2026, 6:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b01d0915148190b77b8a30fa3f796d |
completed | March 10, 2026, 1:30 p.m. |
| NEDg | Description generation | batch_69b01def4f408190a0fcdba125b642bc |
completed | March 10, 2026, 1:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b01e75d3c4819097fd86902eccd8c4 |
completed | March 10, 2026, 1:36 p.m. |
Created at: March 4, 2026, 7:47 p.m.