Triple
T3276853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady and the Tramp |
E68777
|
entity |
| Predicate | storyBy |
P1955
|
FINISHED |
| Object |
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."
|
E362868
|
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: Ward Greene | Statement: [Lady and the Tramp, storyBy, Ward Greene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ward Greene Context triple: [Lady and the Tramp, storyBy, Ward Greene]
-
A.
David Dodd
David Dodd was an American economist, Columbia Business School professor, and influential co-author of the classic value investing text "Security Analysis."
-
B.
Donald McEnery
Donald McEnery is an American screenwriter best known for co-writing the Pixar animated film "A Bug's Life."
-
C.
Estey C. Graham
Estey C. Graham was the wife of influential value-investing pioneer Benjamin Graham.
-
D.
John T. Woolley
John T. Woolley is a political scientist and academic known for co-founding the American Presidency Project, a major online archive of U.S. presidential documents.
-
E.
Hugh G. Jones
Hugh G. Jones was an architect known for his role in designing Toronto’s historic Union Station.
- 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: Ward Greene Triple: [Lady and the Tramp, storyBy, Ward Greene]
Generated description
Ward Greene was an American writer and editor best known for creating the story that inspired Disney’s animated film "Lady and the Tramp."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ward Greene Target entity description: Ward Greene was an American writer and editor best known for creating the story that inspired Disney’s animated film "Lady and the Tramp."
-
A.
David Dodd
David Dodd was an American economist, Columbia Business School professor, and influential co-author of the classic value investing text "Security Analysis."
-
B.
Donald McEnery
Donald McEnery is an American screenwriter best known for co-writing the Pixar animated film "A Bug's Life."
-
C.
Estey C. Graham
Estey C. Graham was the wife of influential value-investing pioneer Benjamin Graham.
-
D.
John T. Woolley
John T. Woolley is a political scientist and academic known for co-founding the American Presidency Project, a major online archive of U.S. presidential documents.
-
E.
Hugh G. Jones
Hugh G. Jones was an architect known for his role in designing Toronto’s historic Union Station.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0128f08819084644f3c8fda2596 |
completed | March 8, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b373911a00819092e4436b6a931d61 |
completed | March 13, 2026, 2:16 a.m. |
| NEDg | Description generation | batch_69b374d51a3c8190846baf5e147287bf |
completed | March 13, 2026, 2:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b37564bd988190a8e00e571dd04001 |
completed | March 13, 2026, 2:24 a.m. |
Created at: March 8, 2026, 3:10 p.m.