Triple
T13603012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Edge |
E324989
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Robert Green
Robert Green is a fictional character who serves as the central protagonist in the survival thriller film "The Edge."
|
E1061565
|
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: Robert Green | Statement: [The Edge, mainCharacter, Robert Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Green Context triple: [The Edge, mainCharacter, Robert Green]
-
A.
Andrew H. Green
Andrew H. Green was a prominent New York civic leader and urban planner known for helping shape Central Park and other major city institutions in the 19th century.
-
B.
John M. Greene
John M. Greene was a 19th-century clergyman and educator whose advocacy and support were instrumental in the founding and shaping of Smith College.
-
C.
Ward Greene
Ward Greene was an American writer and editor best known for creating the story that inspired Disney’s animated film "Lady and the Tramp."
-
D.
Andrew Green
Andrew Green was a British paranormal investigator and author known for his extensive work on ghost hunting and psychical research.
-
E.
Christopher Greenup
Christopher Greenup was an early American politician who served as the third governor of Kentucky in the early 19th century.
- 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: Robert Green Triple: [The Edge, mainCharacter, Robert Green]
Generated description
Robert Green is a fictional character who serves as the central protagonist in the survival thriller film "The Edge."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Robert Green Target entity description: Robert Green is a fictional character who serves as the central protagonist in the survival thriller film "The Edge."
-
A.
Andrew H. Green
Andrew H. Green was a prominent New York civic leader and urban planner known for helping shape Central Park and other major city institutions in the 19th century.
-
B.
John M. Greene
John M. Greene was a 19th-century clergyman and educator whose advocacy and support were instrumental in the founding and shaping of Smith College.
-
C.
Ward Greene
Ward Greene was an American writer and editor best known for creating the story that inspired Disney’s animated film "Lady and the Tramp."
-
D.
Andrew Green
Andrew Green was a British paranormal investigator and author known for his extensive work on ghost hunting and psychical research.
-
E.
Christopher Greenup
Christopher Greenup was an early American politician who served as the third governor of Kentucky in the early 19th century.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb07ca07481909c45da551ea61ab4 |
completed | April 12, 2026, 2:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b05f0e948190b7f22d071ad54283 |
completed | May 3, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f7b138158c8190957d43d529c37fa4 |
completed | May 3, 2026, 8:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7b1e0ab948190a046a68aa5e029a6 |
completed | May 3, 2026, 8:36 p.m. |
Created at: April 9, 2026, 9:49 p.m.