Triple
T7672400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lion King 1½ |
E173778
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Tom Rogers
Tom Rogers is an American screenwriter best known for his work on animated films and television, including Disney projects such as The Lion King 1½.
|
E681731
|
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: Tom Rogers | Statement: [The Lion King 1½, writer, Tom Rogers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Rogers Context triple: [The Lion King 1½, writer, Tom Rogers]
-
A.
Matt Roberts
Matt Roberts was an American guitarist best known as a founding member of the rock band 3 Doors Down.
-
B.
Eric Rogers
Eric Rogers was a British composer and conductor best known for scoring many of the "Carry On" comedy films.
-
C.
Tom Burleson
Tom Burleson is a retired American professional basketball center best known for his shot-blocking and rebounding in the NBA during the 1970s.
-
D.
Mat Rogers
Mat Rogers is an Australian former dual-code rugby international who played both rugby league and rugby union at elite levels, including representing Australia and starring in the NRL.
-
E.
Jim Threapleton
Jim Threapleton is a British film director and former assistant director, known in the public eye largely for his past marriage to actress Kate Winslet.
- 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: Tom Rogers Triple: [The Lion King 1½, writer, Tom Rogers]
Generated description
Tom Rogers is an American screenwriter best known for his work on animated films and television, including Disney projects such as The Lion King 1½.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom Rogers Target entity description: Tom Rogers is an American screenwriter best known for his work on animated films and television, including Disney projects such as The Lion King 1½.
-
A.
Matt Roberts
Matt Roberts was an American guitarist best known as a founding member of the rock band 3 Doors Down.
-
B.
Eric Rogers
Eric Rogers was a British composer and conductor best known for scoring many of the "Carry On" comedy films.
-
C.
Tom Burleson
Tom Burleson is a retired American professional basketball center best known for his shot-blocking and rebounding in the NBA during the 1970s.
-
D.
Mat Rogers
Mat Rogers is an Australian former dual-code rugby international who played both rugby league and rugby union at elite levels, including representing Australia and starring in the NRL.
-
E.
Jim Threapleton
Jim Threapleton is a British film director and former assistant director, known in the public eye largely for his past marriage to actress Kate Winslet.
- 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_69c6995703e0819081de77361b602e78 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701de94208190a7627521211452dc |
completed | March 27, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8a22f74f481909498391bfaf23428 |
completed | March 29, 2026, 3:53 a.m. |
| NEDg | Description generation | batch_69c8a34c93a081908ec3509c3abb3866 |
completed | March 29, 2026, 3:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8a3b4e0a88190ad525c83bd03e09f |
completed | March 29, 2026, 3:59 a.m. |
Created at: March 27, 2026, 4 p.m.