Triple
T9459861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green for Danger |
E228116
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Ronald Adam
Ronald Adam was a British actor and Royal Air Force officer known for his character roles in mid-20th-century films and stage productions.
|
E802646
|
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: Ronald Adam | Statement: [Green for Danger, castMember, Ronald Adam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ronald Adam Context triple: [Green for Danger, castMember, Ronald Adam]
-
A.
Ronald Davidson
Ronald Davidson was an American screenwriter best known for his prolific work on action-packed film serials and B-movie adventures during the mid-20th century.
-
B.
Ronald Bailey
Ronald Bailey is a family member of Pro Football Hall of Fame cornerback Champ Bailey.
-
C.
Ronald Norrish
Ronald Norrish was a British physical chemist and Nobel laureate renowned for his pioneering work in chemical kinetics and photochemistry.
-
D.
Geoffrey Adams
Geoffrey Adams is an actor known for his role in the long-running British television police drama "Dixon of Dock Green."
-
E.
Ronald Ivelaw-Chapman
Ronald Ivelaw-Chapman was a senior Royal Air Force officer who rose to high command during and after the Second World War, playing a key role in the leadership of Britain’s air defense forces.
- 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: Ronald Adam Triple: [Green for Danger, castMember, Ronald Adam]
Generated description
Ronald Adam was a British actor and Royal Air Force officer known for his character roles in mid-20th-century films and stage productions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ronald Adam Target entity description: Ronald Adam was a British actor and Royal Air Force officer known for his character roles in mid-20th-century films and stage productions.
-
A.
Ronald Davidson
Ronald Davidson was an American screenwriter best known for his prolific work on action-packed film serials and B-movie adventures during the mid-20th century.
-
B.
Ronald Bailey
Ronald Bailey is a family member of Pro Football Hall of Fame cornerback Champ Bailey.
-
C.
Ronald Norrish
Ronald Norrish was a British physical chemist and Nobel laureate renowned for his pioneering work in chemical kinetics and photochemistry.
-
D.
Geoffrey Adams
Geoffrey Adams is an actor known for his role in the long-running British television police drama "Dixon of Dock Green."
-
E.
Ronald Ivelaw-Chapman
Ronald Ivelaw-Chapman was a senior Royal Air Force officer who rose to high command during and after the Second World War, playing a key role in the leadership of Britain’s air defense forces.
- 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_69ca843b123881909b0e60028475d12d |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fc916348190aeb3874a89071677 |
completed | April 1, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12ce01b548190a30f6cc7f3084b06 |
completed | April 4, 2026, 3:23 p.m. |
| NEDg | Description generation | batch_69d130c9c89c819080fa98e149a512a9 |
completed | April 4, 2026, 3:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1311c105c8190b94f4ffa348c6b59 |
completed | April 4, 2026, 3:41 p.m. |
Created at: March 30, 2026, 7:52 p.m.