Triple
T14287577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School Reunion |
E354213
|
entity |
| Predicate | featuresActor |
P15562
|
FINISHED |
| Object |
Rod Arthur
Rod Arthur is an actor best known for his role in the British television series "School Reunion."
|
E1091850
|
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: Rod Arthur | Statement: [School Reunion, featuresActor, Rod Arthur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rod Arthur Context triple: [School Reunion, featuresActor, Rod Arthur]
-
A.
John Guillermin
John Guillermin was a British film director known for his work on large-scale adventure and disaster films, including the 1976 remake of King Kong and The Towering Inferno.
-
B.
J. Lee Thompson
J. Lee Thompson was a British film director known for a wide range of popular movies, including war epics, thrillers, and collaborations with major Hollywood stars.
-
C.
Lloyd Taylor
Lloyd Taylor was an architect known for designing Parliament House in Adelaide, South Australia.
-
D.
Gordon Douglas
Gordon Douglas was an American film director known for his prolific work across genres in Hollywood from the 1930s through the 1970s.
-
E.
George Aldrich
George Aldrich is a NASA contamination control specialist known for his long career testing materials for off-gassing to ensure astronaut safety on space missions.
- 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: Rod Arthur Triple: [School Reunion, featuresActor, Rod Arthur]
Generated description
Rod Arthur is an actor best known for his role in the British television series "School Reunion."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rod Arthur Target entity description: Rod Arthur is an actor best known for his role in the British television series "School Reunion."
-
A.
John Guillermin
John Guillermin was a British film director known for his work on large-scale adventure and disaster films, including the 1976 remake of King Kong and The Towering Inferno.
-
B.
J. Lee Thompson
J. Lee Thompson was a British film director known for a wide range of popular movies, including war epics, thrillers, and collaborations with major Hollywood stars.
-
C.
Lloyd Taylor
Lloyd Taylor was an architect known for designing Parliament House in Adelaide, South Australia.
-
D.
Gordon Douglas
Gordon Douglas was an American film director known for his prolific work across genres in Hollywood from the 1930s through the 1970s.
-
E.
George Aldrich
George Aldrich is a NASA contamination control specialist known for his long career testing materials for off-gassing to ensure astronaut safety on space missions.
- 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_69d8278e17088190b328c5a9d4be74ff |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de698023288190b1d705235c2b2ca3 |
completed | April 14, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d1e14d4819091c381f96c43c58b |
completed | May 8, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69fd3e6dde6081908a37817e4dd22ecd |
completed | May 8, 2026, 1:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3f42ccdc81908399d8f9a2f0da31 |
completed | May 8, 2026, 1:41 a.m. |
Created at: April 10, 2026, 1:11 a.m.