Triple
T17907546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wallabee Beetles |
E447739
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Wally |
—
|
NE NERFINISHED |
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: Wally | Statement: [Wallabee Beetles, nickname, Wally]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wally Context triple: [Wallabee Beetles, nickname, Wally]
-
A.
Wally
Wally is a character featured in the educational children's series "Alphabetical Order," likely serving as a playful figure to help teach letters and literacy concepts.
-
B.
Wally
chosen
Wally is a common English diminutive given name, typically derived from names like Walter or Waldemar.
-
C.
Wally
Wally is a character from the comedy film "The Great Outdoors," known for his role in the movie’s humorous family vacation mishaps.
-
D.
Wally Mars
Wally Mars is a central character in the romantic comedy film "The Switch," known as the neurotic best friend whose actions inadvertently lead to an unconventional parenthood twist.
-
E.
Wally Fay
Wally Fay is a supporting character in the 1945 film noir "Mildred Pierce," known as a somewhat sleazy businessman entangled in the story’s web of betrayal and murder.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49e9d458881909e35e1c7a6e85436 |
completed | April 19, 2026, 9:21 a.m. |
Created at: April 10, 2026, 10:19 a.m.