Triple
T7517698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Robin |
E177686
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Tigger |
E352812
|
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: Tigger | Statement: [Christopher Robin, character, Tigger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tigger Context triple: [Christopher Robin, character, Tigger]
-
A.
Tigger
chosen
Tigger is a bouncy, exuberant tiger character from A. A. Milne’s Winnie-the-Pooh stories, known for his boundless energy and cheerful personality.
-
B.
Winnie-the-Pooh
Winnie-the-Pooh is a classic children's character created by A. A. Milne, known as a lovable, honey-obsessed bear who lives in the Hundred Acre Wood with his animal friends.
-
C.
TOM the Tiger
TOM the Tiger is the live Bengal tiger mascot that represents the University of Memphis Tigers athletic programs.
-
D.
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."
-
E.
Teddy
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
- 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_69c69f2891148190a484f3b8222c6f1b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5f6ccc8819080ffd123fdd59a50 |
completed | March 27, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84621ba688190b85ee787b856b138 |
completed | March 28, 2026, 9:20 p.m. |
Created at: March 27, 2026, 3:46 p.m.