Triple
T2173218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Exorcist |
E48469
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Norman Gay
Norman Gay is a film editor best known for his work on the classic horror movie "The Exorcist."
|
E250453
|
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: Norman Gay | Statement: [The Exorcist, editedBy, Norman Gay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norman Gay Context triple: [The Exorcist, editedBy, Norman Gay]
-
A.
Norman Riley
Norman Riley is a distinguished mathematician and fluid dynamicist recognized for his influential contributions to theoretical and applied fluid mechanics.
-
B.
Norman Ferguson
Norman Ferguson was an American animator and film director at Walt Disney Studios, best known for his influential work on classic Disney features during the Golden Age of animation.
-
C.
Norman Puckle
Norman Puckle is the bumbling yet well-meaning protagonist of the 1935 British comedy film "The Bulldog Breed."
-
D.
Norman Winslow
Norman Winslow is an architect best known for his role in designing Portland, Oregon’s iconic public space, Pioneer Courthouse Square.
-
E.
Norman Black
Norman Black is a former American professional basketball player and highly successful coach, best known for his long and decorated career in the Philippine Basketball Association.
- 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: Norman Gay Triple: [The Exorcist, editedBy, Norman Gay]
Generated description
Norman Gay is a film editor best known for his work on the classic horror movie "The Exorcist."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Norman Gay Target entity description: Norman Gay is a film editor best known for his work on the classic horror movie "The Exorcist."
-
A.
Norman Riley
Norman Riley is a distinguished mathematician and fluid dynamicist recognized for his influential contributions to theoretical and applied fluid mechanics.
-
B.
Norman Ferguson
Norman Ferguson was an American animator and film director at Walt Disney Studios, best known for his influential work on classic Disney features during the Golden Age of animation.
-
C.
Norman Puckle
Norman Puckle is the bumbling yet well-meaning protagonist of the 1935 British comedy film "The Bulldog Breed."
-
D.
Norman Winslow
Norman Winslow is an architect best known for his role in designing Portland, Oregon’s iconic public space, Pioneer Courthouse Square.
-
E.
Norman Black
Norman Black is a former American professional basketball player and highly successful coach, best known for his long and decorated career in the Philippine Basketball Association.
- 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_69a88aa3faa48190995b233af6525815 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbecb97a48190834e3e536184bbd1 |
completed | March 7, 2026, 5:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71b142208190a09c8e459b200ecb |
completed | March 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69ae71f7263c819086690d5cf8b2e1a7 |
completed | March 9, 2026, 7:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae734a22f48190a437cbc57c659d38 |
completed | March 9, 2026, 7:14 a.m. |
Created at: March 4, 2026, 7:45 p.m.