Triple
T20589708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Gabble Girls |
E505883
|
entity |
| Predicate | featuresCharacters |
P662
|
FINISHED |
| Object | Uncle Waldo |
—
|
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: Uncle Waldo | Statement: [The Gabble Girls, featuresCharacters, Uncle Waldo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uncle Waldo Context triple: [The Gabble Girls, featuresCharacters, Uncle Waldo]
-
A.
Uncle Waldo
chosen
Uncle Waldo is a musical act best known for performing the song "The Gabble Girls."
-
B.
Waldo
Waldo is a surname of English origin borne by various notable individuals, including figures in American colonial and political history.
-
C.
Waldo
Waldo is Mr. Magoo’s good-natured but often exasperated nephew who frequently appears in the classic Mr. Magoo animated cartoons.
-
D.
Wiarton Willie
Wiarton Willie is a famous Canadian groundhog celebrated for his annual Groundhog Day weather prediction festival in Wiarton, Ontario.
-
E.
Uncle Hugo
Uncle Hugo is a supporting character in the 1986 fantasy drama film "The Boy Who Could Fly," serving as a quirky, often humorous adult figure in the protagonist's life.
- 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_69e0b4b9669c8190b8e81fc72817d42c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a979e4a48190a948165fb0f3b265 |
completed | April 20, 2026, 10:32 p.m. |
Created at: April 16, 2026, 11:40 a.m.