Triple
T5203106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Happy Days |
E117441
|
entity |
| Predicate | protagonist |
P268
|
FINISHED |
| Object | Winnie |
E501006
|
NE FINISHED |
How this triple was built (2 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: Winnie | Statement: [Happy Days, protagonist, Winnie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Winnie Context triple: [Happy Days, protagonist, Winnie]
-
A.
Winnie
chosen
Winnie is a central character from the classic American sitcom "Happy Days," known for her role in the show's nostalgic portrayal of 1950s Midwestern life.
-
B.
Teddy
Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
-
C.
Teddy
Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
-
D.
Bess
Bess is a character in Louisa May Alcott’s novel "Little Men," which continues the story of the March family from "Little Women."
-
E.
Bess
Bess was the familiar nickname of Elizabeth "Bess" Truman, the First Lady of the United States and wife of President Harry S. Truman.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd4463dd3c81909966123f20b79d57 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7a46393c81908da08f4fbfb6147d |
completed | March 20, 2026, 4:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beefc60abc8190a0abcaf8b42dfe3d |
completed | March 21, 2026, 7:21 p.m. |
Created at: March 20, 2026, 1:47 p.m.